Overview

Brought to you by YData

Dataset statistics

Number of variables98
Number of observations202793
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory144.9 MiB
Average record size in memory749.0 B

Variable types

Numeric5
Boolean5
Categorical88

Alerts

Cooling_corr is highly overall correlated with Heating_corrHigh correlation
Heating_corr is highly overall correlated with Cooling_corrHigh correlation
Typeofproperty_condo is highly overall correlated with Typeofproperty_single_family_homeHigh correlation
Typeofproperty_single_family_home is highly overall correlated with Typeofproperty_condoHigh correlation
baths is highly overall correlated with sqftHigh correlation
sqft is highly overall correlated with bathsHigh correlation
status_Active is highly overall correlated with status_For SaleHigh correlation
status_For Sale is highly overall correlated with status_Active and 1 other fieldsHigh correlation
status_Therest is highly overall correlated with status_For SaleHigh correlation
status_Closed is highly imbalanced (> 99.9%) Imbalance
status_Coming Soon is highly imbalanced (99.4%) Imbalance
status_Contingent is highly imbalanced (96.2%) Imbalance
status_For Rent is highly imbalanced (> 99.9%) Imbalance
status_Foreclosure is highly imbalanced (79.4%) Imbalance
status_Pending is highly imbalanced (83.4%) Imbalance
status_Price Change is highly imbalanced (99.1%) Imbalance
status_Under Contract is highly imbalanced (90.5%) Imbalance
state_AZ is highly imbalanced (89.9%) Imbalance
state_CA is highly imbalanced (65.8%) Imbalance
state_CO is highly imbalanced (85.7%) Imbalance
state_DC is highly imbalanced (89.7%) Imbalance
state_DE is highly imbalanced (> 99.9%) Imbalance
state_GA is highly imbalanced (85.7%) Imbalance
state_IA is highly imbalanced (99.0%) Imbalance
state_IL is highly imbalanced (83.0%) Imbalance
state_IN is highly imbalanced (92.3%) Imbalance
state_KY is highly imbalanced (99.7%) Imbalance
state_MA is highly imbalanced (97.1%) Imbalance
state_MD is highly imbalanced (96.1%) Imbalance
state_ME is highly imbalanced (99.8%) Imbalance
state_MI is highly imbalanced (90.1%) Imbalance
state_MO is highly imbalanced (96.8%) Imbalance
state_MS is highly imbalanced (99.9%) Imbalance
state_MT is highly imbalanced (> 99.9%) Imbalance
state_NC is highly imbalanced (67.1%) Imbalance
state_NJ is highly imbalanced (99.5%) Imbalance
state_NV is highly imbalanced (80.0%) Imbalance
state_NY is highly imbalanced (79.4%) Imbalance
state_OH is highly imbalanced (78.0%) Imbalance
state_OK is highly imbalanced (99.9%) Imbalance
state_OR is highly imbalanced (92.5%) Imbalance
state_PA is highly imbalanced (90.2%) Imbalance
state_SC is highly imbalanced (99.8%) Imbalance
state_TN is highly imbalanced (72.6%) Imbalance
state_UT is highly imbalanced (95.8%) Imbalance
state_VA is highly imbalanced (97.7%) Imbalance
state_VT is highly imbalanced (98.2%) Imbalance
state_WA is highly imbalanced (73.9%) Imbalance
state_WI is highly imbalanced (98.8%) Imbalance
Typeofproperty_apartment is highly imbalanced (98.2%) Imbalance
Typeofproperty_historical is highly imbalanced (99.8%) Imbalance
Typeofproperty_land is highly imbalanced (98.1%) Imbalance
Typeofproperty_miscellaneous is highly imbalanced (99.6%) Imbalance
Typeofproperty_mobile_home is highly imbalanced (90.4%) Imbalance
Typeofproperty_modern is highly imbalanced (96.7%) Imbalance
Typeofproperty_multi_family_home is highly imbalanced (81.3%) Imbalance
Typeofproperty_ranch is highly imbalanced (97.6%) Imbalance
Typeofproperty_therest is highly imbalanced (77.3%) Imbalance
Typeofproperty_townhouse is highly imbalanced (63.2%) Imbalance
city_0 is highly imbalanced (95.2%) Imbalance
city_1 is highly imbalanced (67.3%) Imbalance
Year built_0 is highly imbalanced (93.5%) Imbalance

Reproduction

Analysis started2025-06-03 10:30:47.381691
Analysis finished2025-06-03 10:32:23.093956
Duration1 minute and 35.71 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

baths
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4756821
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-06-03T17:32:23.194139image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum30
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90977853
Coefficient of variation (CV)0.36748601
Kurtosis17.216515
Mean2.4756821
Median Absolute Deviation (MAD)0
Skewness1.6470132
Sum502051
Variance0.82769698
MonotonicityNot monotonic
2025-06-03T17:32:23.364334image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 106034
52.3%
3 54127
26.7%
4 22301
 
11.0%
1 16237
 
8.0%
5 3157
 
1.6%
6 653
 
0.3%
7 139
 
0.1%
8 77
 
< 0.1%
12 23
 
< 0.1%
11 12
 
< 0.1%
Other values (10) 33
 
< 0.1%
ValueCountFrequency (%)
1 16237
 
8.0%
2 106034
52.3%
3 54127
26.7%
4 22301
 
11.0%
5 3157
 
1.6%
6 653
 
0.3%
7 139
 
0.1%
8 77
 
< 0.1%
9 6
 
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
30 2
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
20 2
 
< 0.1%
19 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 8
 
< 0.1%
12 23
< 0.1%
11 12
< 0.1%

sqft
Real number (ℝ)

High correlation 

Distinct4315
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1936.6047
Minimum1
Maximum4633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-06-03T17:32:23.569576image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile885
Q11324
median1792
Q32407
95-th percentile3510.4
Maximum4633
Range4632
Interquartile range (IQR)1083

Descriptive statistics

Standard deviation805.56433
Coefficient of variation (CV)0.41596735
Kurtosis0.2662734
Mean1936.6047
Median Absolute Deviation (MAD)523
Skewness0.78684317
Sum3.9272988 × 108
Variance648933.89
MonotonicityNot monotonic
2025-06-03T17:32:23.800691image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 953
 
0.5%
1500 716
 
0.4%
1800 710
 
0.4%
1400 661
 
0.3%
1600 632
 
0.3%
1100 611
 
0.3%
2000 587
 
0.3%
1000 554
 
0.3%
1440 533
 
0.3%
1300 528
 
0.3%
Other values (4305) 196308
96.8%
ValueCountFrequency (%)
1 28
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
10 2
 
< 0.1%
12 1
 
< 0.1%
31 1
 
< 0.1%
40 3
 
< 0.1%
48 1
 
< 0.1%
ValueCountFrequency (%)
4633 1
 
< 0.1%
4632 10
< 0.1%
4631 2
 
< 0.1%
4630 10
< 0.1%
4629 2
 
< 0.1%
4628 1
 
< 0.1%
4627 4
 
< 0.1%
4626 1
 
< 0.1%
4625 10
< 0.1%
4624 7
< 0.1%

target
Real number (ℝ)

Distinct23456
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366163.68
Minimum1000
Maximum1057335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-06-03T17:32:23.990291image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile93483.2
Q1210000
median314900
Q3477549
95-th percentile824500
Maximum1057335
Range1056335
Interquartile range (IQR)267549

Descriptive statistics

Standard deviation217074.29
Coefficient of variation (CV)0.59283403
Kurtosis0.45812314
Mean366163.68
Median Absolute Deviation (MAD)124900
Skewness0.96764424
Sum7.4255431 × 1010
Variance4.7121246 × 1010
MonotonicityNot monotonic
2025-06-03T17:32:24.203319image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225000 1099
 
0.5%
299900 1040
 
0.5%
249900 1039
 
0.5%
275000 1032
 
0.5%
325000 991
 
0.5%
350000 971
 
0.5%
199900 960
 
0.5%
375000 949
 
0.5%
399000 948
 
0.5%
250000 946
 
0.5%
Other values (23446) 192818
95.1%
ValueCountFrequency (%)
1000 293
0.1%
1100 1
 
< 0.1%
1250 1
 
< 0.1%
1299 1
 
< 0.1%
1325 1
 
< 0.1%
1500 1
 
< 0.1%
1695 3
 
< 0.1%
1850 1
 
< 0.1%
1885 1
 
< 0.1%
1950 1
 
< 0.1%
ValueCountFrequency (%)
1057335 1
 
< 0.1%
1056125 2
 
< 0.1%
1056118 1
 
< 0.1%
1055900 1
 
< 0.1%
1055784 1
 
< 0.1%
1055377 2
 
< 0.1%
1055000 7
< 0.1%
1054995 1
 
< 0.1%
1054234 1
 
< 0.1%
1053049 1
 
< 0.1%

pool_corr
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.2 KiB
False
176138 
True
26655 
ValueCountFrequency (%)
False 176138
86.9%
True 26655
 
13.1%
2025-06-03T17:32:24.388613image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Heating_corr
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.2 KiB
True
168157 
False
34636 
ValueCountFrequency (%)
True 168157
82.9%
False 34636
 
17.1%
2025-06-03T17:32:24.517557image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Cooling_corr
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.2 KiB
True
156116 
False
46677 
ValueCountFrequency (%)
True 156116
77.0%
False 46677
 
23.0%
2025-06-03T17:32:24.644604image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.2 KiB
True
140925 
False
61868 
ValueCountFrequency (%)
True 140925
69.5%
False 61868
30.5%
2025-06-03T17:32:24.781665image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.2 KiB
False
136462 
True
66331 
ValueCountFrequency (%)
False 136462
67.3%
True 66331
32.7%
2025-06-03T17:32:24.926342image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

school_rating_mean
Real number (ℝ)

Distinct79
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4995592
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Negative478
Negative (%)0.2%
Memory size1.5 MiB
2025-06-03T17:32:25.092539image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.8
Q13
median4.3
Q35.7
95-th percentile7.7
Maximum9
Range10
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.8203632
Coefficient of variation (CV)0.40456479
Kurtosis-0.48165736
Mean4.4995592
Median Absolute Deviation (MAD)1.3
Skewness0.21884837
Sum912479.1
Variance3.3137222
MonotonicityNot monotonic
2025-06-03T17:32:25.285251image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 12995
 
6.4%
3 12353
 
6.1%
5 10412
 
5.1%
3.3 9481
 
4.7%
3.7 8082
 
4.0%
6 7927
 
3.9%
4.7 7723
 
3.8%
5.3 7713
 
3.8%
2 7328
 
3.6%
4.3 7022
 
3.5%
Other values (69) 111757
55.1%
ValueCountFrequency (%)
-1 478
 
0.2%
0.5 297
 
0.1%
0.7 25
 
< 0.1%
1 3649
1.8%
1.2 38
 
< 0.1%
1.3 1167
 
0.6%
1.4 149
 
0.1%
1.5 1137
 
0.6%
1.6 656
 
0.3%
1.7 2064
1.0%
ValueCountFrequency (%)
9 1188
 
0.6%
8.8 86
 
< 0.1%
8.7 1265
 
0.6%
8.6 35
 
< 0.1%
8.5 540
 
0.3%
8.4 75
 
< 0.1%
8.3 1449
 
0.7%
8.2 258
 
0.1%
8 3805
1.9%
7.8 578
 
0.3%

school_distance_min
Real number (ℝ)

Distinct268
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7828855
Minimum0.02
Maximum2.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-06-03T17:32:25.471091image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.15
Q10.34
median0.6
Q31.1
95-th percentile2.02
Maximum2.69
Range2.67
Interquartile range (IQR)0.76

Descriptive statistics

Standard deviation0.57740188
Coefficient of variation (CV)0.73753043
Kurtosis0.77940645
Mean0.7828855
Median Absolute Deviation (MAD)0.3
Skewness1.1691556
Sum158763.7
Variance0.33339294
MonotonicityNot monotonic
2025-06-03T17:32:25.694667image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 13747
 
6.8%
0.4 13011
 
6.4%
0.2 12057
 
5.9%
0.5 11778
 
5.8%
0.6 9868
 
4.9%
0.7 8409
 
4.1%
0.8 6540
 
3.2%
0.1 6105
 
3.0%
0.9 5733
 
2.8%
1.1 4587
 
2.3%
Other values (258) 110958
54.7%
ValueCountFrequency (%)
0.02 11
 
< 0.1%
0.03 40
 
< 0.1%
0.04 66
 
< 0.1%
0.05 166
 
0.1%
0.06 172
 
0.1%
0.07 242
 
0.1%
0.08 262
 
0.1%
0.09 328
 
0.2%
0.1 6105
3.0%
0.11 478
 
0.2%
ValueCountFrequency (%)
2.69 31
 
< 0.1%
2.68 41
 
< 0.1%
2.67 36
 
< 0.1%
2.66 53
 
< 0.1%
2.65 50
 
< 0.1%
2.64 77
 
< 0.1%
2.63 47
 
< 0.1%
2.62 31
 
< 0.1%
2.61 51
 
< 0.1%
2.6 794
0.4%

status_Active
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
167406 
1.0
35387 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 167406
82.6%
1.0 35387
 
17.4%

Length

2025-06-03T17:32:25.856292image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:25.990914image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 167406
82.6%
1.0 35387
 
17.4%

Most occurring characters

ValueCountFrequency (%)
0 370199
60.9%
. 202793
33.3%
1 35387
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 370199
60.9%
. 202793
33.3%
1 35387
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 370199
60.9%
. 202793
33.3%
1 35387
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 370199
60.9%
. 202793
33.3%
1 35387
 
5.8%

status_Closed
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202792 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202792
> 99.9%
1.0 1
 
< 0.1%

Length

2025-06-03T17:32:26.131806image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:26.260548image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202792
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405585
66.7%
. 202793
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405585
66.7%
. 202793
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405585
66.7%
. 202793
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405585
66.7%
. 202793
33.3%
1 1
 
< 0.1%

status_Coming Soon
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202703 
1.0
 
90

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202703
> 99.9%
1.0 90
 
< 0.1%

Length

2025-06-03T17:32:26.392088image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:26.518917image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202703
> 99.9%
1.0 90
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405496
66.7%
. 202793
33.3%
1 90
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405496
66.7%
. 202793
33.3%
1 90
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405496
66.7%
. 202793
33.3%
1 90
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405496
66.7%
. 202793
33.3%
1 90
 
< 0.1%

status_Contingent
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
201976 
1.0
 
817

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201976
99.6%
1.0 817
 
0.4%

Length

2025-06-03T17:32:26.663287image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:26.791731image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201976
99.6%
1.0 817
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 404769
66.5%
. 202793
33.3%
1 817
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 404769
66.5%
. 202793
33.3%
1 817
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 404769
66.5%
. 202793
33.3%
1 817
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 404769
66.5%
. 202793
33.3%
1 817
 
0.1%

status_For Rent
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202789 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202789
> 99.9%
1.0 4
 
< 0.1%

Length

2025-06-03T17:32:26.920183image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:27.045783image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202789
> 99.9%
1.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405582
66.7%
. 202793
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405582
66.7%
. 202793
33.3%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405582
66.7%
. 202793
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405582
66.7%
. 202793
33.3%
1 4
 
< 0.1%

status_For Sale
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1.0
124081 
0.0
78712 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 124081
61.2%
0.0 78712
38.8%

Length

2025-06-03T17:32:27.168019image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:27.293858image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 124081
61.2%
0.0 78712
38.8%

Most occurring characters

ValueCountFrequency (%)
0 281505
46.3%
. 202793
33.3%
1 124081
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 281505
46.3%
. 202793
33.3%
1 124081
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 281505
46.3%
. 202793
33.3%
1 124081
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 281505
46.3%
. 202793
33.3%
1 124081
20.4%

status_Foreclosure
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
196230 
1.0
 
6563

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 196230
96.8%
1.0 6563
 
3.2%

Length

2025-06-03T17:32:27.437564image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:27.581073image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 196230
96.8%
1.0 6563
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 399023
65.6%
. 202793
33.3%
1 6563
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 399023
65.6%
. 202793
33.3%
1 6563
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 399023
65.6%
. 202793
33.3%
1 6563
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 399023
65.6%
. 202793
33.3%
1 6563
 
1.1%

status_Pending
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
197834 
1.0
 
4959

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 197834
97.6%
1.0 4959
 
2.4%

Length

2025-06-03T17:32:27.725872image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:27.858107image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 197834
97.6%
1.0 4959
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 400627
65.9%
. 202793
33.3%
1 4959
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 400627
65.9%
. 202793
33.3%
1 4959
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 400627
65.9%
. 202793
33.3%
1 4959
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 400627
65.9%
. 202793
33.3%
1 4959
 
0.8%

status_Price Change
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202643 
1.0
 
150

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202643
99.9%
1.0 150
 
0.1%

Length

2025-06-03T17:32:27.995360image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:28.121875image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202643
99.9%
1.0 150
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 405436
66.6%
. 202793
33.3%
1 150
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405436
66.6%
. 202793
33.3%
1 150
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405436
66.6%
. 202793
33.3%
1 150
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405436
66.6%
. 202793
33.3%
1 150
 
< 0.1%

status_Therest
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
174537 
1.0
28256 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 174537
86.1%
1.0 28256
 
13.9%

Length

2025-06-03T17:32:28.279268image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:28.414496image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 174537
86.1%
1.0 28256
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 377330
62.0%
. 202793
33.3%
1 28256
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 377330
62.0%
. 202793
33.3%
1 28256
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 377330
62.0%
. 202793
33.3%
1 28256
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 377330
62.0%
. 202793
33.3%
1 28256
 
4.6%

status_Under Contract
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
200308 
1.0
 
2485

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200308
98.8%
1.0 2485
 
1.2%

Length

2025-06-03T17:32:28.551177image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:28.679542image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200308
98.8%
1.0 2485
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 403101
66.3%
. 202793
33.3%
1 2485
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 403101
66.3%
. 202793
33.3%
1 2485
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 403101
66.3%
. 202793
33.3%
1 2485
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 403101
66.3%
. 202793
33.3%
1 2485
 
0.4%

state_AZ
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
200124 
1.0
 
2669

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200124
98.7%
1.0 2669
 
1.3%

Length

2025-06-03T17:32:28.816716image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:28.946420image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200124
98.7%
1.0 2669
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 402917
66.2%
. 202793
33.3%
1 2669
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 402917
66.2%
. 202793
33.3%
1 2669
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 402917
66.2%
. 202793
33.3%
1 2669
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 402917
66.2%
. 202793
33.3%
1 2669
 
0.4%

state_CA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
189868 
1.0
 
12925

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 189868
93.6%
1.0 12925
 
6.4%

Length

2025-06-03T17:32:29.094700image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:29.224220image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 189868
93.6%
1.0 12925
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 392661
64.5%
. 202793
33.3%
1 12925
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 392661
64.5%
. 202793
33.3%
1 12925
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 392661
64.5%
. 202793
33.3%
1 12925
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 392661
64.5%
. 202793
33.3%
1 12925
 
2.1%

state_CO
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
198675 
1.0
 
4118

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 198675
98.0%
1.0 4118
 
2.0%

Length

2025-06-03T17:32:29.367320image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:29.530137image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 198675
98.0%
1.0 4118
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 401468
66.0%
. 202793
33.3%
1 4118
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 401468
66.0%
. 202793
33.3%
1 4118
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 401468
66.0%
. 202793
33.3%
1 4118
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 401468
66.0%
. 202793
33.3%
1 4118
 
0.7%

state_DC
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
200064 
1.0
 
2729

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200064
98.7%
1.0 2729
 
1.3%

Length

2025-06-03T17:32:29.695178image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:29.851663image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200064
98.7%
1.0 2729
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 402857
66.2%
. 202793
33.3%
1 2729
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 402857
66.2%
. 202793
33.3%
1 2729
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 402857
66.2%
. 202793
33.3%
1 2729
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 402857
66.2%
. 202793
33.3%
1 2729
 
0.4%

state_DE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202789 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202789
> 99.9%
1.0 4
 
< 0.1%

Length

2025-06-03T17:32:30.010287image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:30.164366image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202789
> 99.9%
1.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405582
66.7%
. 202793
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405582
66.7%
. 202793
33.3%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405582
66.7%
. 202793
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405582
66.7%
. 202793
33.3%
1 4
 
< 0.1%

state_FL
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
145515 
1.0
57278 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 145515
71.8%
1.0 57278
 
28.2%

Length

2025-06-03T17:32:30.324651image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:30.476745image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 145515
71.8%
1.0 57278
 
28.2%

Most occurring characters

ValueCountFrequency (%)
0 348308
57.3%
. 202793
33.3%
1 57278
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 348308
57.3%
. 202793
33.3%
1 57278
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 348308
57.3%
. 202793
33.3%
1 57278
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 348308
57.3%
. 202793
33.3%
1 57278
 
9.4%

state_GA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
198688 
1.0
 
4105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 198688
98.0%
1.0 4105
 
2.0%

Length

2025-06-03T17:32:30.641825image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:30.771185image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 198688
98.0%
1.0 4105
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 401481
66.0%
. 202793
33.3%
1 4105
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 401481
66.0%
. 202793
33.3%
1 4105
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 401481
66.0%
. 202793
33.3%
1 4105
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 401481
66.0%
. 202793
33.3%
1 4105
 
0.7%

state_IA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202628 
1.0
 
165

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202628
99.9%
1.0 165
 
0.1%

Length

2025-06-03T17:32:30.914482image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:31.051946image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202628
99.9%
1.0 165
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 405421
66.6%
. 202793
33.3%
1 165
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405421
66.6%
. 202793
33.3%
1 165
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405421
66.6%
. 202793
33.3%
1 165
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405421
66.6%
. 202793
33.3%
1 165
 
< 0.1%

state_IL
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
197688 
1.0
 
5105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 197688
97.5%
1.0 5105
 
2.5%

Length

2025-06-03T17:32:31.199863image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:31.353779image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 197688
97.5%
1.0 5105
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 400481
65.8%
. 202793
33.3%
1 5105
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 400481
65.8%
. 202793
33.3%
1 5105
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 400481
65.8%
. 202793
33.3%
1 5105
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 400481
65.8%
. 202793
33.3%
1 5105
 
0.8%

state_IN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
200873 
1.0
 
1920

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200873
99.1%
1.0 1920
 
0.9%

Length

2025-06-03T17:32:31.506910image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:31.642917image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200873
99.1%
1.0 1920
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 403666
66.4%
. 202793
33.3%
1 1920
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 403666
66.4%
. 202793
33.3%
1 1920
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 403666
66.4%
. 202793
33.3%
1 1920
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 403666
66.4%
. 202793
33.3%
1 1920
 
0.3%

state_KY
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202740 
1.0
 
53

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202740
> 99.9%
1.0 53
 
< 0.1%

Length

2025-06-03T17:32:31.791144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:31.925365image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202740
> 99.9%
1.0 53
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405533
66.7%
. 202793
33.3%
1 53
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405533
66.7%
. 202793
33.3%
1 53
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405533
66.7%
. 202793
33.3%
1 53
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405533
66.7%
. 202793
33.3%
1 53
 
< 0.1%

state_MA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202205 
1.0
 
588

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202205
99.7%
1.0 588
 
0.3%

Length

2025-06-03T17:32:32.062592image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:32.194932image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202205
99.7%
1.0 588
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 404998
66.6%
. 202793
33.3%
1 588
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 404998
66.6%
. 202793
33.3%
1 588
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 404998
66.6%
. 202793
33.3%
1 588
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 404998
66.6%
. 202793
33.3%
1 588
 
0.1%

state_MD
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
201958 
1.0
 
835

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201958
99.6%
1.0 835
 
0.4%

Length

2025-06-03T17:32:32.341769image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:32.479380image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201958
99.6%
1.0 835
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 404751
66.5%
. 202793
33.3%
1 835
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 404751
66.5%
. 202793
33.3%
1 835
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 404751
66.5%
. 202793
33.3%
1 835
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 404751
66.5%
. 202793
33.3%
1 835
 
0.1%

state_ME
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202759 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202759
> 99.9%
1.0 34
 
< 0.1%

Length

2025-06-03T17:32:32.618329image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:32.757472image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202759
> 99.9%
1.0 34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405552
66.7%
. 202793
33.3%
1 34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405552
66.7%
. 202793
33.3%
1 34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405552
66.7%
. 202793
33.3%
1 34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405552
66.7%
. 202793
33.3%
1 34
 
< 0.1%

state_MI
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
200179 
1.0
 
2614

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200179
98.7%
1.0 2614
 
1.3%

Length

2025-06-03T17:32:32.904730image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:33.037449image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200179
98.7%
1.0 2614
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 402972
66.2%
. 202793
33.3%
1 2614
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 402972
66.2%
. 202793
33.3%
1 2614
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 402972
66.2%
. 202793
33.3%
1 2614
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 402972
66.2%
. 202793
33.3%
1 2614
 
0.4%

state_MO
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202127 
1.0
 
666

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202127
99.7%
1.0 666
 
0.3%

Length

2025-06-03T17:32:33.174142image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:33.334244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202127
99.7%
1.0 666
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 404920
66.6%
. 202793
33.3%
1 666
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 404920
66.6%
. 202793
33.3%
1 666
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 404920
66.6%
. 202793
33.3%
1 666
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 404920
66.6%
. 202793
33.3%
1 666
 
0.1%

state_MS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202775 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202775
> 99.9%
1.0 18
 
< 0.1%

Length

2025-06-03T17:32:33.500875image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:33.631881image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202775
> 99.9%
1.0 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405568
66.7%
. 202793
33.3%
1 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405568
66.7%
. 202793
33.3%
1 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405568
66.7%
. 202793
33.3%
1 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405568
66.7%
. 202793
33.3%
1 18
 
< 0.1%

state_MT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202791 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202791
> 99.9%
1.0 2
 
< 0.1%

Length

2025-06-03T17:32:33.777268image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:33.905741image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202791
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405584
66.7%
. 202793
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405584
66.7%
. 202793
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405584
66.7%
. 202793
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405584
66.7%
. 202793
33.3%
1 2
 
< 0.1%

state_NC
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
190567 
1.0
 
12226

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 190567
94.0%
1.0 12226
 
6.0%

Length

2025-06-03T17:32:34.035380image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:34.162990image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 190567
94.0%
1.0 12226
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 393360
64.7%
. 202793
33.3%
1 12226
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 393360
64.7%
. 202793
33.3%
1 12226
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 393360
64.7%
. 202793
33.3%
1 12226
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 393360
64.7%
. 202793
33.3%
1 12226
 
2.0%

state_NJ
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202719 
1.0
 
74

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202719
> 99.9%
1.0 74
 
< 0.1%

Length

2025-06-03T17:32:34.295777image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:34.443338image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202719
> 99.9%
1.0 74
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405512
66.7%
. 202793
33.3%
1 74
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405512
66.7%
. 202793
33.3%
1 74
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405512
66.7%
. 202793
33.3%
1 74
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405512
66.7%
. 202793
33.3%
1 74
 
< 0.1%

state_NV
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
196463 
1.0
 
6330

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 196463
96.9%
1.0 6330
 
3.1%

Length

2025-06-03T17:32:34.533634image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:34.620158image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 196463
96.9%
1.0 6330
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 399256
65.6%
. 202793
33.3%
1 6330
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 399256
65.6%
. 202793
33.3%
1 6330
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 399256
65.6%
. 202793
33.3%
1 6330
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 399256
65.6%
. 202793
33.3%
1 6330
 
1.0%

state_NY
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
196226 
1.0
 
6567

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 196226
96.8%
1.0 6567
 
3.2%

Length

2025-06-03T17:32:34.713261image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:34.800702image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 196226
96.8%
1.0 6567
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 399019
65.6%
. 202793
33.3%
1 6567
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 399019
65.6%
. 202793
33.3%
1 6567
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 399019
65.6%
. 202793
33.3%
1 6567
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 399019
65.6%
. 202793
33.3%
1 6567
 
1.1%

state_OH
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
195667 
1.0
 
7126

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 195667
96.5%
1.0 7126
 
3.5%

Length

2025-06-03T17:32:34.897033image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:34.983207image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 195667
96.5%
1.0 7126
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 398460
65.5%
. 202793
33.3%
1 7126
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 398460
65.5%
. 202793
33.3%
1 7126
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 398460
65.5%
. 202793
33.3%
1 7126
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 398460
65.5%
. 202793
33.3%
1 7126
 
1.2%

state_OK
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202777 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202777
> 99.9%
1.0 16
 
< 0.1%

Length

2025-06-03T17:32:35.081145image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:35.167804image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202777
> 99.9%
1.0 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405570
66.7%
. 202793
33.3%
1 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405570
66.7%
. 202793
33.3%
1 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405570
66.7%
. 202793
33.3%
1 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405570
66.7%
. 202793
33.3%
1 16
 
< 0.1%

state_OR
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
200926 
1.0
 
1867

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 200926
99.1%
1.0 1867
 
0.9%

Length

2025-06-03T17:32:35.264026image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:35.363196image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200926
99.1%
1.0 1867
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 403719
66.4%
. 202793
33.3%
1 1867
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 403719
66.4%
. 202793
33.3%
1 1867
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 403719
66.4%
. 202793
33.3%
1 1867
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 403719
66.4%
. 202793
33.3%
1 1867
 
0.3%

state_PA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
200230 
1.0
 
2563

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200230
98.7%
1.0 2563
 
1.3%

Length

2025-06-03T17:32:35.476873image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:35.580683image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200230
98.7%
1.0 2563
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 403023
66.2%
. 202793
33.3%
1 2563
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 403023
66.2%
. 202793
33.3%
1 2563
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 403023
66.2%
. 202793
33.3%
1 2563
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 403023
66.2%
. 202793
33.3%
1 2563
 
0.4%

state_SC
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202766 
1.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202766
> 99.9%
1.0 27
 
< 0.1%

Length

2025-06-03T17:32:35.700799image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:35.800867image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202766
> 99.9%
1.0 27
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405559
66.7%
. 202793
33.3%
1 27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405559
66.7%
. 202793
33.3%
1 27
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405559
66.7%
. 202793
33.3%
1 27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405559
66.7%
. 202793
33.3%
1 27
 
< 0.1%

state_TN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
193245 
1.0
 
9548

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 193245
95.3%
1.0 9548
 
4.7%

Length

2025-06-03T17:32:35.926722image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:36.058807image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 193245
95.3%
1.0 9548
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 396038
65.1%
. 202793
33.3%
1 9548
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 396038
65.1%
. 202793
33.3%
1 9548
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 396038
65.1%
. 202793
33.3%
1 9548
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 396038
65.1%
. 202793
33.3%
1 9548
 
1.6%

state_TX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
153063 
1.0
49730 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 153063
75.5%
1.0 49730
 
24.5%

Length

2025-06-03T17:32:36.179589image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:36.283565image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 153063
75.5%
1.0 49730
 
24.5%

Most occurring characters

ValueCountFrequency (%)
0 355856
58.5%
. 202793
33.3%
1 49730
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 355856
58.5%
. 202793
33.3%
1 49730
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 355856
58.5%
. 202793
33.3%
1 49730
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 355856
58.5%
. 202793
33.3%
1 49730
 
8.2%

state_UT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
201867 
1.0
 
926

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 201867
99.5%
1.0 926
 
0.5%

Length

2025-06-03T17:32:36.386514image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:36.483037image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 201867
99.5%
1.0 926
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 404660
66.5%
. 202793
33.3%
1 926
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 404660
66.5%
. 202793
33.3%
1 926
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 404660
66.5%
. 202793
33.3%
1 926
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 404660
66.5%
. 202793
33.3%
1 926
 
0.2%

state_VA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202335 
1.0
 
458

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202335
99.8%
1.0 458
 
0.2%

Length

2025-06-03T17:32:36.586307image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:36.687888image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202335
99.8%
1.0 458
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 405128
66.6%
. 202793
33.3%
1 458
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405128
66.6%
. 202793
33.3%
1 458
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405128
66.6%
. 202793
33.3%
1 458
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405128
66.6%
. 202793
33.3%
1 458
 
0.1%

state_VT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202451 
1.0
 
342

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202451
99.8%
1.0 342
 
0.2%

Length

2025-06-03T17:32:36.786022image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:36.877081image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202451
99.8%
1.0 342
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 405244
66.6%
. 202793
33.3%
1 342
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405244
66.6%
. 202793
33.3%
1 342
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405244
66.6%
. 202793
33.3%
1 342
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405244
66.6%
. 202793
33.3%
1 342
 
0.1%

state_WA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
193838 
1.0
 
8955

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 193838
95.6%
1.0 8955
 
4.4%

Length

2025-06-03T17:32:36.974241image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:37.063429image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 193838
95.6%
1.0 8955
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 396631
65.2%
. 202793
33.3%
1 8955
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 396631
65.2%
. 202793
33.3%
1 8955
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 396631
65.2%
. 202793
33.3%
1 8955
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 396631
65.2%
. 202793
33.3%
1 8955
 
1.5%

state_WI
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202583 
1.0
 
210

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202583
99.9%
1.0 210
 
0.1%

Length

2025-06-03T17:32:37.164721image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:37.258005image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202583
99.9%
1.0 210
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 405376
66.6%
. 202793
33.3%
1 210
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405376
66.6%
. 202793
33.3%
1 210
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405376
66.6%
. 202793
33.3%
1 210
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405376
66.6%
. 202793
33.3%
1 210
 
< 0.1%

Typeofproperty_apartment
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202451 
1.0
 
342

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202451
99.8%
1.0 342
 
0.2%

Length

2025-06-03T17:32:37.363244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:37.467419image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202451
99.8%
1.0 342
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 405244
66.6%
. 202793
33.3%
1 342
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405244
66.6%
. 202793
33.3%
1 342
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405244
66.6%
. 202793
33.3%
1 342
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405244
66.6%
. 202793
33.3%
1 342
 
0.1%

Typeofproperty_condo
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
169297 
1.0
33496 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 169297
83.5%
1.0 33496
 
16.5%

Length

2025-06-03T17:32:37.568548image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:37.672249image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 169297
83.5%
1.0 33496
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 372090
61.2%
. 202793
33.3%
1 33496
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 372090
61.2%
. 202793
33.3%
1 33496
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 372090
61.2%
. 202793
33.3%
1 33496
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 372090
61.2%
. 202793
33.3%
1 33496
 
5.5%

Typeofproperty_historical
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202767 
1.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202767
> 99.9%
1.0 26
 
< 0.1%

Length

2025-06-03T17:32:37.792220image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:37.895483image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202767
> 99.9%
1.0 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405560
66.7%
. 202793
33.3%
1 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405560
66.7%
. 202793
33.3%
1 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405560
66.7%
. 202793
33.3%
1 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405560
66.7%
. 202793
33.3%
1 26
 
< 0.1%

Typeofproperty_land
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202423 
1.0
 
370

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202423
99.8%
1.0 370
 
0.2%

Length

2025-06-03T17:32:37.997092image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:38.120318image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202423
99.8%
1.0 370
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 405216
66.6%
. 202793
33.3%
1 370
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405216
66.6%
. 202793
33.3%
1 370
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405216
66.6%
. 202793
33.3%
1 370
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405216
66.6%
. 202793
33.3%
1 370
 
0.1%

Typeofproperty_miscellaneous
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202733 
1.0
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202733
> 99.9%
1.0 60
 
< 0.1%

Length

2025-06-03T17:32:38.242336image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:38.344196image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202733
> 99.9%
1.0 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 405526
66.7%
. 202793
33.3%
1 60
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405526
66.7%
. 202793
33.3%
1 60
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405526
66.7%
. 202793
33.3%
1 60
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405526
66.7%
. 202793
33.3%
1 60
 
< 0.1%

Typeofproperty_mobile_home
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
200289 
1.0
 
2504

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 200289
98.8%
1.0 2504
 
1.2%

Length

2025-06-03T17:32:38.478870image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:38.585971image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 200289
98.8%
1.0 2504
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 403082
66.3%
. 202793
33.3%
1 2504
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 403082
66.3%
. 202793
33.3%
1 2504
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 403082
66.3%
. 202793
33.3%
1 2504
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 403082
66.3%
. 202793
33.3%
1 2504
 
0.4%

Typeofproperty_modern
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202094 
1.0
 
699

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202094
99.7%
1.0 699
 
0.3%

Length

2025-06-03T17:32:38.696954image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:38.797843image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202094
99.7%
1.0 699
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 404887
66.6%
. 202793
33.3%
1 699
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 404887
66.6%
. 202793
33.3%
1 699
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 404887
66.6%
. 202793
33.3%
1 699
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 404887
66.6%
. 202793
33.3%
1 699
 
0.1%

Typeofproperty_multi_family_home
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
196997 
1.0
 
5796

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 196997
97.1%
1.0 5796
 
2.9%

Length

2025-06-03T17:32:38.904274image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:39.007178image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 196997
97.1%
1.0 5796
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 399790
65.7%
. 202793
33.3%
1 5796
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 399790
65.7%
. 202793
33.3%
1 5796
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 399790
65.7%
. 202793
33.3%
1 5796
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 399790
65.7%
. 202793
33.3%
1 5796
 
1.0%

Typeofproperty_ranch
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
202321 
1.0
 
472

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202321
99.8%
1.0 472
 
0.2%

Length

2025-06-03T17:32:39.109241image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:39.209110image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202321
99.8%
1.0 472
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 405114
66.6%
. 202793
33.3%
1 472
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 405114
66.6%
. 202793
33.3%
1 472
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 405114
66.6%
. 202793
33.3%
1 472
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 405114
66.6%
. 202793
33.3%
1 472
 
0.1%

Typeofproperty_single_family_home
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1.0
137247 
0.0
65546 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 137247
67.7%
0.0 65546
32.3%

Length

2025-06-03T17:32:39.306449image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:40.957660image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 137247
67.7%
0.0 65546
32.3%

Most occurring characters

ValueCountFrequency (%)
0 268339
44.1%
. 202793
33.3%
1 137247
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 268339
44.1%
. 202793
33.3%
1 137247
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 268339
44.1%
. 202793
33.3%
1 137247
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 268339
44.1%
. 202793
33.3%
1 137247
22.6%

Typeofproperty_therest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
195334 
1.0
 
7459

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 195334
96.3%
1.0 7459
 
3.7%

Length

2025-06-03T17:32:41.058021image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:41.150909image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 195334
96.3%
1.0 7459
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 398127
65.4%
. 202793
33.3%
1 7459
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 398127
65.4%
. 202793
33.3%
1 7459
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 398127
65.4%
. 202793
33.3%
1 7459
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 398127
65.4%
. 202793
33.3%
1 7459
 
1.2%

Typeofproperty_townhouse
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
188471 
1.0
 
14322

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608379
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 188471
92.9%
1.0 14322
 
7.1%

Length

2025-06-03T17:32:41.247517image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:41.339760image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 188471
92.9%
1.0 14322
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 391264
64.3%
. 202793
33.3%
1 14322
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 391264
64.3%
. 202793
33.3%
1 14322
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 391264
64.3%
. 202793
33.3%
1 14322
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 391264
64.3%
. 202793
33.3%
1 14322
 
2.4%

city_0
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
201711 
1
 
1082

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 201711
99.5%
1 1082
 
0.5%

Length

2025-06-03T17:32:41.436308image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:41.526081image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 201711
99.5%
1 1082
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 201711
99.5%
1 1082
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 201711
99.5%
1 1082
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 201711
99.5%
1 1082
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 201711
99.5%
1 1082
 
0.5%

city_1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
190664 
1
 
12129

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 190664
94.0%
1 12129
 
6.0%

Length

2025-06-03T17:32:41.623645image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:41.727133image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 190664
94.0%
1 12129
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 190664
94.0%
1 12129
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 190664
94.0%
1 12129
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 190664
94.0%
1 12129
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 190664
94.0%
1 12129
 
6.0%

city_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
174774 
1
28019 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174774
86.2%
1 28019
 
13.8%

Length

2025-06-03T17:32:41.829220image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:41.936136image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 174774
86.2%
1 28019
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 174774
86.2%
1 28019
 
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174774
86.2%
1 28019
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174774
86.2%
1 28019
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174774
86.2%
1 28019
 
13.8%

city_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
162070 
1
40723 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162070
79.9%
1 40723
 
20.1%

Length

2025-06-03T17:32:42.043238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:42.145114image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 162070
79.9%
1 40723
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 162070
79.9%
1 40723
 
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 162070
79.9%
1 40723
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 162070
79.9%
1 40723
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 162070
79.9%
1 40723
 
20.1%

city_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
135767 
1
67026 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 135767
66.9%
1 67026
33.1%

Length

2025-06-03T17:32:42.242760image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:42.331866image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 135767
66.9%
1 67026
33.1%

Most occurring characters

ValueCountFrequency (%)
0 135767
66.9%
1 67026
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 135767
66.9%
1 67026
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 135767
66.9%
1 67026
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 135767
66.9%
1 67026
33.1%

city_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
126358 
1
76435 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 126358
62.3%
1 76435
37.7%

Length

2025-06-03T17:32:42.432564image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:42.529738image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 126358
62.3%
1 76435
37.7%

Most occurring characters

ValueCountFrequency (%)
0 126358
62.3%
1 76435
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 126358
62.3%
1 76435
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 126358
62.3%
1 76435
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 126358
62.3%
1 76435
37.7%

city_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
108661 
0
94132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 108661
53.6%
0 94132
46.4%

Length

2025-06-03T17:32:42.628969image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:42.719207image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 108661
53.6%
0 94132
46.4%

Most occurring characters

ValueCountFrequency (%)
1 108661
53.6%
0 94132
46.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 108661
53.6%
0 94132
46.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 108661
53.6%
0 94132
46.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 108661
53.6%
0 94132
46.4%

city_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
118743 
1
84050 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 118743
58.6%
1 84050
41.4%

Length

2025-06-03T17:32:42.816543image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:42.909083image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 118743
58.6%
1 84050
41.4%

Most occurring characters

ValueCountFrequency (%)
0 118743
58.6%
1 84050
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118743
58.6%
1 84050
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118743
58.6%
1 84050
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118743
58.6%
1 84050
41.4%

city_8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
102218 
0
100575 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 102218
50.4%
0 100575
49.6%

Length

2025-06-03T17:32:43.010648image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:43.107513image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 102218
50.4%
0 100575
49.6%

Most occurring characters

ValueCountFrequency (%)
1 102218
50.4%
0 100575
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 102218
50.4%
0 100575
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 102218
50.4%
0 100575
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 102218
50.4%
0 100575
49.6%

city_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
109343 
0
93450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 109343
53.9%
0 93450
46.1%

Length

2025-06-03T17:32:43.209745image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:43.301695image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 109343
53.9%
0 93450
46.1%

Most occurring characters

ValueCountFrequency (%)
1 109343
53.9%
0 93450
46.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 109343
53.9%
0 93450
46.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 109343
53.9%
0 93450
46.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 109343
53.9%
0 93450
46.1%

city_10
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
117170 
1
85623 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 117170
57.8%
1 85623
42.2%

Length

2025-06-03T17:32:43.398442image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:43.491396image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 117170
57.8%
1 85623
42.2%

Most occurring characters

ValueCountFrequency (%)
0 117170
57.8%
1 85623
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 117170
57.8%
1 85623
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 117170
57.8%
1 85623
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 117170
57.8%
1 85623
42.2%

zipcode_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
179877 
1
22916 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 179877
88.7%
1 22916
 
11.3%

Length

2025-06-03T17:32:43.595920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:43.697722image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 179877
88.7%
1 22916
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0 179877
88.7%
1 22916
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 179877
88.7%
1 22916
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 179877
88.7%
1 22916
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 179877
88.7%
1 22916
 
11.3%

zipcode_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
145586 
1
57207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 145586
71.8%
1 57207
 
28.2%

Length

2025-06-03T17:32:43.802097image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:43.893301image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 145586
71.8%
1 57207
 
28.2%

Most occurring characters

ValueCountFrequency (%)
0 145586
71.8%
1 57207
 
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 145586
71.8%
1 57207
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 145586
71.8%
1 57207
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 145586
71.8%
1 57207
 
28.2%

zipcode_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
124872 
1
77921 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124872
61.6%
1 77921
38.4%

Length

2025-06-03T17:32:43.991173image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:44.081849image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 124872
61.6%
1 77921
38.4%

Most occurring characters

ValueCountFrequency (%)
0 124872
61.6%
1 77921
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 124872
61.6%
1 77921
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 124872
61.6%
1 77921
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 124872
61.6%
1 77921
38.4%

zipcode_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
112021 
1
90772 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112021
55.2%
1 90772
44.8%

Length

2025-06-03T17:32:44.185573image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:44.278007image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 112021
55.2%
1 90772
44.8%

Most occurring characters

ValueCountFrequency (%)
0 112021
55.2%
1 90772
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 112021
55.2%
1 90772
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 112021
55.2%
1 90772
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 112021
55.2%
1 90772
44.8%

zipcode_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
108958 
1
93835 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 108958
53.7%
1 93835
46.3%

Length

2025-06-03T17:32:44.376453image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:44.471081image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 108958
53.7%
1 93835
46.3%

Most occurring characters

ValueCountFrequency (%)
0 108958
53.7%
1 93835
46.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 108958
53.7%
1 93835
46.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 108958
53.7%
1 93835
46.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 108958
53.7%
1 93835
46.3%

zipcode_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
106556 
1
96237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106556
52.5%
1 96237
47.5%

Length

2025-06-03T17:32:44.573333image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:44.668684image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 106556
52.5%
1 96237
47.5%

Most occurring characters

ValueCountFrequency (%)
0 106556
52.5%
1 96237
47.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 106556
52.5%
1 96237
47.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 106556
52.5%
1 96237
47.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 106556
52.5%
1 96237
47.5%

zipcode_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
103189 
1
99604 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 103189
50.9%
1 99604
49.1%

Length

2025-06-03T17:32:44.768571image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:44.860997image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 103189
50.9%
1 99604
49.1%

Most occurring characters

ValueCountFrequency (%)
0 103189
50.9%
1 99604
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103189
50.9%
1 99604
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103189
50.9%
1 99604
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103189
50.9%
1 99604
49.1%

zipcode_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
104677 
1
98116 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 104677
51.6%
1 98116
48.4%

Length

2025-06-03T17:32:44.959731image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:45.053037image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 104677
51.6%
1 98116
48.4%

Most occurring characters

ValueCountFrequency (%)
0 104677
51.6%
1 98116
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 104677
51.6%
1 98116
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 104677
51.6%
1 98116
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 104677
51.6%
1 98116
48.4%

zipcode_8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
101598 
1
101195 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101598
50.1%
1 101195
49.9%

Length

2025-06-03T17:32:45.150767image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:45.243564image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 101598
50.1%
1 101195
49.9%

Most occurring characters

ValueCountFrequency (%)
0 101598
50.1%
1 101195
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 101598
50.1%
1 101195
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 101598
50.1%
1 101195
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 101598
50.1%
1 101195
49.9%

zipcode_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
103119 
1
99674 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 103119
50.8%
1 99674
49.2%

Length

2025-06-03T17:32:45.339735image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:45.430027image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 103119
50.8%
1 99674
49.2%

Most occurring characters

ValueCountFrequency (%)
0 103119
50.8%
1 99674
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103119
50.8%
1 99674
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103119
50.8%
1 99674
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103119
50.8%
1 99674
49.2%

zipcode_10
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
105694 
0
97099 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 105694
52.1%
0 97099
47.9%

Length

2025-06-03T17:32:45.527355image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:45.613482image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 105694
52.1%
0 97099
47.9%

Most occurring characters

ValueCountFrequency (%)
1 105694
52.1%
0 97099
47.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 105694
52.1%
0 97099
47.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 105694
52.1%
0 97099
47.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 105694
52.1%
0 97099
47.9%

zipcode_11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
104733 
0
98060 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 104733
51.6%
0 98060
48.4%

Length

2025-06-03T17:32:45.710140image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:45.799626image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 104733
51.6%
0 98060
48.4%

Most occurring characters

ValueCountFrequency (%)
1 104733
51.6%
0 98060
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 104733
51.6%
0 98060
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 104733
51.6%
0 98060
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 104733
51.6%
0 98060
48.4%

Year built_0
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
201225 
1
 
1568

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 201225
99.2%
1 1568
 
0.8%

Length

2025-06-03T17:32:45.897627image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:45.985967image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 201225
99.2%
1 1568
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 201225
99.2%
1 1568
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 201225
99.2%
1 1568
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 201225
99.2%
1 1568
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 201225
99.2%
1 1568
 
0.8%

Year built_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
153471 
1
49322 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 153471
75.7%
1 49322
 
24.3%

Length

2025-06-03T17:32:46.081109image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:46.168242image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 153471
75.7%
1 49322
 
24.3%

Most occurring characters

ValueCountFrequency (%)
0 153471
75.7%
1 49322
 
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 153471
75.7%
1 49322
 
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 153471
75.7%
1 49322
 
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 153471
75.7%
1 49322
 
24.3%

Year built_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
122514 
1
80279 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 122514
60.4%
1 80279
39.6%

Length

2025-06-03T17:32:46.267040image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:46.362699image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 122514
60.4%
1 80279
39.6%

Most occurring characters

ValueCountFrequency (%)
0 122514
60.4%
1 80279
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 122514
60.4%
1 80279
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 122514
60.4%
1 80279
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 122514
60.4%
1 80279
39.6%

Year built_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
118470 
1
84323 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 118470
58.4%
1 84323
41.6%

Length

2025-06-03T17:32:46.468722image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:46.568659image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 118470
58.4%
1 84323
41.6%

Most occurring characters

ValueCountFrequency (%)
0 118470
58.4%
1 84323
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118470
58.4%
1 84323
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118470
58.4%
1 84323
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118470
58.4%
1 84323
41.6%

Year built_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
109581 
1
93212 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 109581
54.0%
1 93212
46.0%

Length

2025-06-03T17:32:46.672998image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:46.768553image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 109581
54.0%
1 93212
46.0%

Most occurring characters

ValueCountFrequency (%)
0 109581
54.0%
1 93212
46.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 109581
54.0%
1 93212
46.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 109581
54.0%
1 93212
46.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 109581
54.0%
1 93212
46.0%

Year built_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
114921 
1
87872 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 114921
56.7%
1 87872
43.3%

Length

2025-06-03T17:32:46.868189image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:46.961830image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 114921
56.7%
1 87872
43.3%

Most occurring characters

ValueCountFrequency (%)
0 114921
56.7%
1 87872
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 114921
56.7%
1 87872
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 114921
56.7%
1 87872
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 114921
56.7%
1 87872
43.3%

Year built_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
109203 
1
93590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 109203
53.8%
1 93590
46.2%

Length

2025-06-03T17:32:47.056935image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:47.149394image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 109203
53.8%
1 93590
46.2%

Most occurring characters

ValueCountFrequency (%)
0 109203
53.8%
1 93590
46.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 109203
53.8%
1 93590
46.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 109203
53.8%
1 93590
46.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 109203
53.8%
1 93590
46.2%

Year built_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
104106 
0
98687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 104106
51.3%
0 98687
48.7%

Length

2025-06-03T17:32:47.246791image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T17:32:47.339225image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 104106
51.3%
0 98687
48.7%

Most occurring characters

ValueCountFrequency (%)
1 104106
51.3%
0 98687
48.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 104106
51.3%
0 98687
48.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 104106
51.3%
0 98687
48.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 104106
51.3%
0 98687
48.7%

Interactions

2025-06-03T17:32:17.786215image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:15.087372image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:15.777926image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:16.419200image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:17.095340image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:17.924793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:15.235916image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:15.910986image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:16.555480image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:17.223923image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:18.052007image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:15.368260image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:16.030598image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:16.684243image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:17.381005image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:18.191209image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:15.508741image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:16.161129image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:16.823946image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:17.524569image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:18.322055image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:15.639019image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:16.292964image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:16.957880image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-06-03T17:32:17.652903image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2025-06-03T17:32:47.538213image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Cooling_corrHeating_corrParking_corrTypeofproperty_apartmentTypeofproperty_condoTypeofproperty_historicalTypeofproperty_landTypeofproperty_miscellaneousTypeofproperty_mobile_homeTypeofproperty_modernTypeofproperty_multi_family_homeTypeofproperty_ranchTypeofproperty_single_family_homeTypeofproperty_therestTypeofproperty_townhouseYear built_0Year built_1Year built_2Year built_3Year built_4Year built_5Year built_6Year built_7bathscity_0city_1city_10city_2city_3city_4city_5city_6city_7city_8city_9fireplace_corrpool_corrschool_distance_minschool_rating_meansqftstate_AZstate_CAstate_COstate_DCstate_DEstate_FLstate_GAstate_IAstate_ILstate_INstate_KYstate_MAstate_MDstate_MEstate_MIstate_MOstate_MSstate_MTstate_NCstate_NJstate_NVstate_NYstate_OHstate_OKstate_ORstate_PAstate_SCstate_TNstate_TXstate_UTstate_VAstate_VTstate_WAstate_WIstatus_Activestatus_Closedstatus_Coming Soonstatus_Contingentstatus_For Rentstatus_For Salestatus_Foreclosurestatus_Pendingstatus_Price Changestatus_Thereststatus_Under Contracttargetzipcode_0zipcode_1zipcode_10zipcode_11zipcode_2zipcode_3zipcode_4zipcode_5zipcode_6zipcode_7zipcode_8zipcode_9
Cooling_corr1.0000.5880.3750.0140.0880.0050.0140.0070.0060.0040.1540.0050.0080.0590.0040.0570.0100.1330.0600.1290.1190.1190.1260.0250.0300.0760.0900.0670.0680.0780.1200.0080.0590.0170.0360.1190.0700.0760.0460.0390.0160.0540.0140.0190.0060.1970.0000.0110.0090.0200.0020.0230.0120.0200.0920.0250.0020.0030.0250.0280.0500.1970.0280.0040.0580.0130.0080.0560.0510.0420.0030.0580.2040.0590.0760.0000.0000.0050.0000.0490.0610.0470.0140.1930.0350.1300.0890.0710.0200.0030.0140.0090.0080.0210.0070.0000.0010.003
Heating_corr0.5881.0000.4680.0050.0560.0220.0070.0060.0090.0230.0090.0270.0150.1360.0110.0110.0760.1390.1190.1140.1430.1250.1220.0130.0100.0420.0660.0440.0100.0450.0350.0400.0480.0030.0020.1420.0410.0400.1020.0780.0000.0020.0130.0050.0000.1200.0130.0280.0090.0000.0030.0220.0050.0030.0330.0260.0000.0040.0650.0070.0460.0820.0540.0000.0210.0320.0120.0580.0680.0150.0050.0160.1710.0700.2400.0000.0000.0050.0000.0860.0100.0810.0060.1650.0210.0930.0040.0040.0050.0070.0020.0130.0080.0350.0090.0110.0060.007
Parking_corr0.3750.4681.0000.0370.0240.0160.0060.0100.0220.0890.0340.0730.1330.2910.0090.0130.0270.1060.0650.0950.0790.0940.0960.0400.0070.0150.0420.0300.0030.0820.0370.0440.0620.0030.0290.1400.0900.0440.0620.0390.0170.0570.0380.0070.0050.0180.0180.0260.0420.0040.0230.0140.0220.0020.0050.0280.0130.0020.0260.0000.0730.0550.0670.0130.0330.0280.0050.0040.0170.0090.0160.0140.0650.0060.3890.0000.0110.0410.0000.1550.0210.1390.0370.2510.0550.0930.0200.0100.0140.0000.0190.0010.0060.0190.0130.0020.0050.013
Typeofproperty_apartment0.0140.0050.0371.0000.0180.0000.0000.0000.0030.0000.0060.0000.0590.0070.0110.0000.0000.0000.0000.0000.0070.0020.0010.0110.0000.0030.0300.0060.0110.0200.0210.0210.0130.0170.0250.0050.0120.0090.0170.0640.0040.0060.0020.0000.0000.0190.0050.0000.0030.0030.0000.0000.0000.0000.0040.0000.0000.0000.0080.0000.0070.0040.0070.0000.0020.0030.0000.0070.0000.0000.0000.0000.0980.0000.0450.0000.0000.0000.0130.0310.0050.0000.0140.0000.0030.0130.0040.0110.0060.0080.0110.0080.0120.0080.0110.0100.0020.005
Typeofproperty_condo0.0880.0560.0240.0181.0000.0040.0190.0070.0500.0260.0760.0210.6440.0870.1230.0090.0020.0400.0060.0540.0790.0490.0070.1170.0050.0260.0090.0280.0280.0030.0240.0310.0700.0160.0160.1300.1130.0730.1350.4240.0240.0170.0300.1070.0080.1430.0040.0050.0660.0210.0060.0470.0340.0040.0270.0000.0030.0000.0530.0190.0130.0130.0320.0020.0110.0190.0000.0410.1450.0030.0210.0000.0000.0020.0200.0000.0040.0040.0010.0210.0370.0060.0110.0660.0320.0800.0080.0420.0270.0100.0050.0030.0090.0490.0120.0120.0130.016
Typeofproperty_historical0.0050.0220.0160.0000.0041.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0040.0000.0000.0000.0060.0000.0000.0000.0000.0000.0080.0030.0050.0070.0070.0100.0060.0090.0110.0110.0030.0060.0110.0080.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0140.0000.0000.0070.0030.0000.0120.0030.0060.0050.0000.0000.0070.0030.0030.0050.0000.0090.008
Typeofproperty_land0.0140.0070.0060.0000.0190.0001.0000.0000.0040.0000.0070.0000.0620.0080.0110.0070.0090.0020.0120.0030.0070.0000.0000.0100.0000.0020.0060.0000.0050.0020.0030.0000.0000.0060.0000.0110.0000.0100.0070.0110.0040.0070.0010.0010.0000.0060.0160.0000.0040.0010.0000.0000.0000.0000.0010.0000.0000.0000.0040.0000.0050.0000.0000.0000.0010.0040.0000.0000.0030.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0300.0000.0060.0000.0170.0040.0360.0000.0060.0000.0010.0020.0000.0030.0030.0030.0070.0000.004
Typeofproperty_miscellaneous0.0070.0060.0100.0000.0070.0000.0001.0000.0000.0000.0000.0000.0240.0010.0040.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0040.0070.0000.0110.0000.0000.0110.0000.0130.0180.0050.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0010.0010.0000.0000.0000.0000.0020.0090.0000.0000.0000.0020.0000.0070.0000.0000.0000.0000.0190.0000.0000.0000.0390.0000.0180.0000.0000.0000.0120.0000.0020.0040.0030.0020.0000.0000.005
Typeofproperty_mobile_home0.0060.0090.0220.0030.0500.0000.0040.0001.0000.0060.0190.0040.1620.0220.0310.0090.0020.0080.0120.0020.0240.0040.0050.0410.0190.0170.0180.0420.0340.0090.0210.0120.0080.0070.0070.0480.0410.0450.0230.0970.0090.0930.0100.0120.0000.0360.0130.0010.0170.0080.0000.0040.0060.0070.0060.0050.0000.0060.0170.0000.0080.0170.0150.0000.0040.0120.0000.0150.0570.0150.0010.0000.0300.0020.0010.0000.0000.0000.0000.0190.0100.0090.0000.0270.0030.2120.0240.0180.0110.0150.0160.0050.0110.0030.0120.0100.0040.000
Typeofproperty_modern0.0040.0230.0890.0000.0260.0000.0000.0000.0061.0000.0100.0000.0850.0110.0160.0000.0000.0040.0070.0120.0010.0000.0170.0140.0010.0110.0010.0120.0120.0000.0030.0260.0140.0000.0110.0110.0230.0180.0120.0180.0040.0130.0130.0600.0000.0080.0080.0000.0040.0050.0000.0010.0100.0000.0060.0010.0000.0000.0080.0000.0100.0100.0080.0000.0050.0060.0000.0050.0120.0030.0420.0000.0120.0000.1260.0000.0000.0000.0000.0740.0100.0070.0000.0230.0060.0280.0170.0190.0040.0060.0070.0040.0100.0060.0000.0070.0070.007
Typeofproperty_multi_family_home0.1540.0090.0340.0060.0760.0000.0070.0000.0190.0101.0000.0080.2480.0330.0470.0810.0960.0020.0550.0410.0100.0020.0180.0740.0000.0090.0100.0150.0270.0330.0370.0240.0310.0140.0560.0680.0570.1180.1050.0630.0200.0440.0140.0110.0000.0560.0190.0020.0890.0090.0000.0230.0110.0000.0340.0080.0000.0000.0350.0010.0250.2130.0600.0000.0040.0450.0000.0280.0670.0120.0080.0130.0210.0230.0390.0000.0000.0040.0010.0580.0080.0060.0030.0370.0130.1300.0310.0390.0040.0100.0340.0150.0090.0070.0030.0090.0060.003
Typeofproperty_ranch0.0050.0270.0730.0000.0210.0000.0000.0000.0040.0000.0081.0000.0700.0090.0130.0030.0000.0100.0080.0000.0030.0000.0020.0150.0020.0100.0000.0000.0000.0000.0280.0000.0000.0050.0100.0100.0190.0080.0180.0160.0660.0120.0060.0050.0000.0000.0060.0000.0230.0040.0000.0000.0010.0000.0050.0000.0000.0000.0270.0000.0080.0080.0070.0000.0030.0040.0000.0000.0120.0010.0000.0000.0100.0000.1030.0000.0000.0000.0000.0600.0080.0060.0000.0180.0040.0210.0160.0200.0050.0070.0030.0060.0000.0020.0090.0150.0060.007
Typeofproperty_single_family_home0.0080.0150.1330.0590.6440.0160.0620.0240.1620.0850.2480.0701.0000.2830.3990.0120.0240.0090.0050.0260.0690.0480.0070.0790.0080.0500.0030.0510.0600.0180.0300.0700.0930.0120.0120.1770.0440.0540.1290.3500.0270.0240.0460.1240.0050.0950.0110.0130.0690.0440.0090.0350.0160.0030.0340.0140.0060.0000.0330.0080.0280.0550.0360.0040.0150.1000.0020.0650.1340.0080.0210.0020.0110.0020.0860.0000.0050.0160.0050.0430.0590.0420.0090.0200.0080.1120.0270.0340.0130.0120.0090.0030.0100.0430.0200.0180.0020.007
Typeofproperty_therest0.0590.1360.2910.0070.0870.0000.0080.0010.0220.0110.0330.0090.2831.0000.0540.0080.0050.0060.0110.0020.0100.0000.0180.0280.0050.0300.0000.0210.0340.0100.0280.0200.0030.0090.0380.0130.0760.0280.0350.0390.0190.0400.0420.0310.0000.0000.0280.0050.0030.0180.0010.0040.0050.0000.0220.0090.0000.0000.0370.0020.0340.0320.0000.0000.0180.0210.0000.0000.0460.0120.0170.0070.0230.0050.3820.0050.0030.0100.0000.2370.0260.0270.0580.0560.0210.0270.0490.0360.0360.0000.0330.0090.0020.0120.0140.0000.0020.000
Typeofproperty_townhouse0.0040.0110.0090.0110.1230.0000.0110.0040.0310.0160.0470.0130.3990.0541.0000.0070.0180.0390.0340.0070.0160.0150.0060.0400.0090.0250.0110.0420.0370.0170.0100.0470.0420.0230.0060.0560.0010.0210.0470.1040.0320.0130.0220.0340.0000.0110.0680.0070.0200.0210.0030.0120.0140.0020.0220.0050.0000.0000.0130.0040.0120.0200.0480.0000.0060.2060.0010.0330.0000.0050.0080.0110.0130.0080.1220.0000.0000.0170.0000.1100.0300.0420.0060.0270.0310.0250.0240.0010.0100.0080.0010.0150.0170.0000.0000.0090.0120.016
Year built_00.0570.0110.0130.0000.0090.0000.0070.0000.0090.0000.0810.0030.0120.0080.0071.0000.0480.0550.0440.0240.0090.0260.0090.0000.0070.0090.0000.0080.0030.0360.0240.0150.0140.0100.0130.0000.0290.0500.0430.0060.0080.0100.0030.0190.0000.0480.0030.0040.0570.0150.0000.0480.0000.0050.0220.0370.0000.0000.0150.0000.0150.0620.0620.0000.0060.0310.0000.0110.0430.0080.0080.0590.0070.0170.0040.0000.0000.0030.0000.0130.0060.0000.0000.0270.0000.0520.0390.0250.0030.0080.0200.0090.0040.0000.0000.0060.0020.009
Year built_10.0100.0760.0270.0000.0020.0040.0090.0000.0020.0000.0960.0000.0240.0050.0180.0481.0000.1220.1100.0170.1020.0010.0260.0430.0040.0080.0060.0000.0000.0370.0340.0220.0440.0030.0250.0440.0340.0980.0940.0800.0200.0240.0000.0700.0000.0480.0150.0100.0570.0170.0000.0280.0040.0030.0620.0310.0000.0000.0410.0000.0280.0580.0770.0030.0010.0720.0000.0130.0650.0150.0050.0000.0060.0200.0060.0000.0000.0110.0000.0000.0350.0070.0030.0270.0040.1240.0400.0330.0050.0050.0240.0120.0060.0100.0030.0100.0060.003
Year built_20.1330.1390.1060.0000.0400.0000.0020.0050.0080.0040.0020.0100.0090.0060.0390.0550.1221.0000.0580.0960.0450.0500.0580.0260.0000.0000.0250.0000.0140.0170.0100.0050.0270.0060.0040.0490.0280.0120.0280.0620.0000.0140.0210.0040.0000.0670.0020.0000.0200.0120.0000.0040.0000.0000.0000.0060.0010.0000.0140.0000.0350.0050.0060.0000.0000.0180.0020.0250.0530.0000.0100.0000.0170.0010.0240.0000.0000.0040.0010.0000.0300.0070.0000.0040.0030.0330.0000.0040.0040.0000.0050.0000.0060.0070.0070.0040.0000.010
Year built_30.0600.1190.0650.0000.0060.0000.0120.0000.0120.0070.0550.0080.0050.0110.0340.0440.1100.0581.0000.0290.1320.0230.1130.0550.0060.0030.0140.0040.0050.0000.0070.0170.0260.0050.0300.0530.0130.0680.0640.1010.0100.0100.0000.0120.0000.0150.0000.0060.0280.0070.0000.0130.0170.0030.0220.0090.0000.0000.0330.0000.0010.0650.0340.0000.0060.0510.0050.0180.0650.0000.0030.0000.0190.0120.0330.0000.0000.0070.0010.0190.0360.0000.0050.0090.0000.0980.0310.0200.0000.0000.0170.0040.0080.0090.0070.0170.0040.000
Year built_40.1290.1140.0950.0000.0540.0000.0030.0000.0020.0120.0410.0000.0260.0020.0070.0240.0170.0960.0291.0000.0190.1970.1130.0280.0000.0070.0220.0120.0170.0250.0160.0090.0000.0190.0230.0090.0220.0450.0230.0140.0230.0080.0060.0050.0000.0400.0060.0080.0080.0210.0000.0130.0020.0000.0180.0050.0010.0000.0040.0030.0060.0450.0290.0010.0040.0540.0040.0260.0190.0020.0040.0070.0080.0000.0120.0000.0000.0040.0030.0270.0120.0120.0040.0480.0120.0520.0190.0170.0080.0040.0100.0080.0020.0090.0070.0010.0120.008
Year built_50.1190.1430.0790.0070.0790.0060.0070.0000.0240.0010.0100.0030.0690.0100.0160.0090.1020.0450.1320.0191.0000.0100.0870.0440.0020.0000.0320.0000.0110.0080.0180.0090.0400.0190.0040.0720.0480.0200.0350.0810.0030.0180.0050.0010.0020.0510.0160.0000.0090.0040.0000.0060.0000.0000.0050.0000.0000.0000.0300.0010.0090.0030.0060.0020.0120.0090.0020.0280.0320.0000.0070.0080.0140.0000.0040.0000.0010.0060.0020.0120.0230.0070.0040.0050.0040.0720.0100.0000.0010.0070.0080.0000.0000.0120.0080.0080.0100.000
Year built_60.1190.1250.0940.0020.0490.0000.0000.0000.0040.0000.0020.0000.0480.0000.0150.0260.0010.0500.0230.1970.0101.0000.1160.0110.0040.0090.0160.0270.0140.0220.0200.0030.0090.0130.0090.0220.0190.0160.0290.0580.0190.0030.0000.0050.0030.0350.0010.0000.0080.0140.0000.0020.0080.0000.0220.0020.0050.0000.0180.0040.0100.0000.0370.0000.0000.0080.0060.0190.0070.0050.0000.0000.0030.0020.0250.0000.0000.0080.0030.0200.0060.0090.0050.0520.0110.0290.0090.0040.0060.0070.0040.0000.0000.0090.0000.0050.0140.000
Year built_70.1260.1220.0960.0010.0070.0000.0000.0000.0050.0170.0180.0020.0070.0180.0060.0090.0260.0580.1130.1130.0870.1161.0000.0180.0030.0020.0160.0030.0120.0110.0070.0120.0040.0000.0150.0400.0250.0120.0080.0160.0040.0090.0000.0110.0030.0070.0000.0000.0060.0130.0000.0000.0040.0000.0160.0040.0030.0000.0070.0090.0090.0210.0160.0000.0000.0230.0050.0260.0080.0000.0050.0000.0120.0000.0080.0000.0010.0000.0030.0410.0060.0230.0050.0330.0230.0290.0080.0000.0030.0000.0000.0030.0040.0020.0000.0070.0040.000
baths0.0250.0130.0400.0110.1170.0000.0100.0000.0410.0140.0740.0150.0790.0280.0400.0000.0430.0260.0550.0280.0440.0110.0181.0000.0090.0100.0170.0050.0160.0130.0090.0210.0380.0240.0180.1020.0450.0750.1720.6490.0210.0420.0140.0020.0000.0850.0580.0050.0070.0020.0000.0110.0000.0000.0190.0000.0000.0000.0540.0000.0000.0630.0120.0000.0000.0080.0000.0210.0990.0000.0000.0070.0400.0000.0190.0000.0000.0100.0000.0460.0440.0330.0000.0070.0000.4110.0260.0120.0220.0140.0200.0270.0120.0180.0250.0080.0210.008
city_00.0300.0100.0070.0000.0050.0000.0000.0000.0190.0010.0000.0020.0080.0050.0090.0070.0040.0000.0060.0000.0020.0040.0030.0091.0000.0180.0080.0000.0250.0140.0100.0150.0100.0000.0000.0000.0000.0270.0220.0040.0080.0190.0050.0080.0530.0210.0060.0160.0090.0050.0180.0120.0660.0070.0080.0000.0000.0000.0060.0000.0050.0430.0190.0000.0060.0000.0080.0010.0210.0090.0640.0600.0020.0000.0260.0000.0000.0080.0000.0110.0000.0000.0000.0150.0010.0290.1370.0650.0040.0040.0120.0050.0080.0030.0100.0050.0000.000
city_10.0760.0420.0150.0030.0260.0000.0020.0000.0170.0110.0090.0100.0500.0300.0250.0090.0080.0000.0030.0070.0000.0090.0020.0100.0181.0000.0370.0860.1350.0560.0440.0260.0700.0000.0000.0270.0190.0210.0700.0150.0000.0970.0390.0290.0000.1060.0180.0480.0140.0080.0090.0530.1040.0050.0700.0030.0000.0000.0390.0030.0100.0290.0860.0000.0100.0080.0410.0190.0660.0520.0650.0780.0660.0090.0690.0030.0150.0290.0000.0370.0190.0390.0000.0590.0050.0570.2980.1080.0160.0090.0460.0350.0150.0010.0110.0050.0090.004
city_100.0900.0660.0420.0300.0090.0080.0060.0000.0180.0010.0100.0000.0030.0000.0110.0000.0060.0250.0140.0220.0320.0160.0160.0170.0080.0371.0000.0430.0440.2150.0920.0210.2900.1660.0280.0570.0500.0500.1000.0260.0370.1140.0410.1370.0030.1630.1120.0140.0990.0950.0080.0380.0050.0000.0700.0290.0070.0000.0930.0220.0860.1200.0310.0070.0430.0630.0000.0360.0690.0170.0070.0000.1280.0310.0570.0000.0010.0070.0010.0220.0110.0090.0270.0640.0670.0720.0690.0020.0420.0290.0160.0320.0020.0540.0130.0190.0370.065
city_20.0670.0440.0300.0060.0280.0030.0000.0040.0420.0120.0150.0000.0510.0210.0420.0080.0000.0000.0040.0120.0000.0270.0030.0050.0000.0860.0431.0000.1630.0590.0610.0340.0200.0300.0090.0380.0160.0410.0650.0310.0240.1000.0350.0470.0000.0860.0210.0290.0030.0210.0000.0090.0560.0000.0410.0180.0020.0000.0480.0070.0610.0290.1270.0020.0300.0080.0260.0080.0850.0090.0290.0440.0650.0560.0730.0010.0030.0700.0000.0250.0240.0360.0040.0930.0210.0470.1510.1050.0230.0420.1160.0300.0260.0410.0050.0280.0050.035
city_30.0680.0100.0030.0110.0280.0050.0050.0000.0340.0120.0270.0000.0600.0340.0370.0030.0000.0140.0050.0170.0110.0140.0120.0160.0250.1350.0440.1631.0000.0820.0740.0290.0760.0130.0420.0340.0070.0360.0600.0230.1530.1410.0280.0580.0000.0260.0410.0350.0570.0270.0260.0780.0390.0220.0020.1020.0030.0040.0260.0090.0830.0000.0540.0030.0080.0100.0140.0870.1380.0930.0470.0360.0170.0000.0090.0000.0130.0220.0000.0420.0290.0100.0100.0960.0060.0670.1570.1210.0090.0340.0110.0590.0140.0290.0220.0100.0110.002
city_40.0780.0450.0820.0200.0030.0070.0020.0040.0090.0000.0330.0000.0180.0100.0170.0360.0370.0170.0000.0250.0080.0220.0110.0130.0140.0560.2150.0590.0821.0000.0700.2730.1260.1000.1060.0040.0780.0540.0500.0280.0800.0000.0650.1660.0050.1110.0530.0120.1020.0570.0180.0540.0240.0010.0090.0260.0130.0020.1550.0080.0350.0520.1180.0120.0490.1330.0000.1200.2190.0250.0140.0240.1510.0350.1600.0000.0080.0190.0000.0430.0080.0740.0190.1830.0910.0680.0680.0170.0000.0240.0590.0380.0420.0460.0440.0610.0200.029
city_50.1200.0350.0370.0210.0240.0070.0030.0070.0210.0030.0370.0280.0300.0280.0100.0240.0340.0100.0070.0160.0180.0200.0070.0090.0100.0440.0920.0610.0740.0701.0000.0260.0050.0630.1640.0110.0170.0550.1030.0130.0460.1150.0920.1500.0010.0880.1330.0120.1570.1180.0030.0360.0410.0000.0950.0350.0060.0010.0150.0240.1320.0150.0290.0060.0620.1270.0080.0510.2030.0190.0180.0220.0990.0170.0110.0000.0050.0020.0000.0960.0340.0200.0190.1670.0210.0870.0900.0460.0480.0000.0670.0000.0540.0580.0230.0180.0240.005
city_60.0080.0400.0440.0210.0310.0100.0000.0000.0120.0260.0240.0000.0700.0200.0470.0150.0220.0050.0170.0090.0090.0030.0120.0210.0150.0260.0210.0340.0290.2730.0261.0000.0090.0650.0250.0560.0500.0650.1060.0360.0000.0770.0740.1250.0030.0660.0760.0180.1610.0680.0100.0180.0180.0070.0420.0290.0090.0000.0340.0160.0980.0610.0590.0090.0660.0880.0080.0570.0390.0300.0050.0020.0720.0130.1140.0000.0050.0270.0020.0670.0000.0390.0200.0680.0540.0500.0150.0170.0180.0230.0090.0450.0180.0310.0100.0060.0090.037
city_70.0590.0480.0620.0130.0700.0060.0000.0110.0080.0140.0310.0000.0930.0030.0420.0140.0440.0270.0260.0000.0400.0090.0040.0380.0100.0700.2900.0200.0760.1260.0050.0091.0000.1550.0490.0840.0440.0560.1320.0760.0380.0050.0470.0980.0040.1510.1180.0080.1170.0570.0100.0230.0030.0000.0340.0440.0070.0000.2000.0160.1260.0950.0590.0070.0750.0760.0000.1040.0500.0420.0080.0170.0000.0130.0490.0000.0050.0110.0000.0070.0220.0220.0270.0890.0780.0570.0150.0080.0180.0120.0760.0120.0220.0550.0210.0000.0600.080
city_80.0170.0030.0030.0170.0160.0090.0060.0000.0070.0000.0140.0050.0120.0090.0230.0100.0030.0060.0050.0190.0190.0130.0000.0240.0000.0000.1660.0300.0130.1000.0630.0650.1551.0000.0760.0720.0000.0150.0760.0450.0630.0070.0910.1160.0030.1060.0970.0030.1290.0840.0080.0320.0210.0000.0670.0300.0090.0000.1570.0150.1040.0200.0000.0080.0870.0840.0110.0830.0550.0360.0130.0130.0330.0260.0120.0000.0000.0180.0030.0140.0010.0140.0220.0450.0560.0250.0020.0120.0180.0430.0060.0020.0060.0200.0350.0150.0100.065
city_90.0360.0020.0290.0250.0160.0110.0000.0000.0070.0110.0560.0100.0120.0380.0060.0130.0250.0040.0300.0230.0040.0090.0150.0180.0000.0000.0280.0090.0420.1060.1640.0250.0490.0761.0000.0090.0150.0450.0970.0400.0270.0000.0590.1080.0020.0610.1030.0000.1270.0910.0060.0240.0090.0070.0630.0530.0090.0000.0300.0200.1570.0570.0000.0090.0690.1020.0100.1540.1680.0370.0100.0060.0130.0120.0220.0000.0000.0090.0020.0760.0160.0050.0240.0880.0050.0330.0290.0410.0130.0310.0040.0050.0360.0340.0140.0000.0120.036
fireplace_corr0.1190.1420.1400.0050.1300.0110.0110.0110.0480.0110.0680.0100.1770.0130.0560.0000.0440.0490.0530.0090.0720.0220.0400.1020.0000.0270.0570.0380.0340.0040.0110.0560.0840.0720.0091.0000.0070.0570.1340.2610.0300.0190.0190.0470.0000.2900.0370.0000.0020.0190.0090.0120.0200.0000.0140.0130.0000.0000.1210.0050.0200.0640.0300.0000.0390.0450.0000.1370.1230.0040.0020.0280.1020.0200.0110.0000.0020.0240.0000.0330.0070.0130.0000.0910.0570.1000.0130.0430.0140.0110.0130.0060.0020.0010.0310.0210.0000.011
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state_TN0.0560.0580.0040.0070.0410.0000.0000.0020.0150.0050.0280.0000.0650.0000.0330.0110.0130.0250.0180.0260.0280.0190.0260.0210.0010.0190.0360.0080.0870.1200.0510.0570.1040.0830.1540.1370.0590.0540.0600.0570.0250.0580.0320.0260.0000.1390.0320.0060.0360.0210.0020.0120.0140.0000.0250.0120.0000.0000.0560.0030.0400.0410.0420.0000.0210.0250.0001.0000.1270.0150.0100.0090.0480.0060.0820.0000.0000.0080.0000.0130.0170.0280.0050.0870.1160.0480.0290.0010.0270.0000.0290.0190.0000.0120.0500.0370.0080.022
state_TX0.0510.0680.0170.0000.1450.0180.0030.0090.0570.0120.0670.0120.1340.0460.0000.0430.0650.0530.0650.0190.0320.0070.0080.0990.0210.0660.0690.0850.1380.2190.2030.0390.0500.0550.1680.1230.0450.0890.1540.1850.0660.1490.0820.0660.0000.3580.0820.0160.0920.0560.0090.0310.0360.0070.0650.0330.0040.0000.1440.0100.1020.1040.1090.0040.0550.0640.0060.1271.0000.0380.0270.0230.1220.0180.0320.0000.0010.0340.0000.0310.0790.0580.0420.1680.0580.1510.0860.0340.0000.0020.0450.0110.0430.0200.0110.0540.0080.000
state_UT0.0420.0150.0090.0000.0030.0000.0000.0000.0150.0030.0120.0010.0080.0120.0050.0080.0150.0000.0000.0020.0000.0050.0000.0000.0090.0520.0170.0090.0930.0250.0190.0300.0420.0360.0370.0040.0170.0240.0270.0140.0070.0170.0090.0070.0000.0420.0090.0000.0100.0060.0000.0020.0030.0000.0070.0020.0000.0000.0170.0000.0120.0120.0130.0000.0060.0070.0000.0150.0381.0000.0010.0000.0140.0000.0300.0000.0000.0030.0000.0450.0090.0100.0000.0170.0060.0290.0350.0290.0000.0060.0090.0140.0120.0070.0040.0050.0160.019
state_VA0.0030.0050.0160.0000.0210.0000.0000.0000.0010.0420.0080.0000.0210.0170.0080.0080.0050.0100.0030.0040.0070.0000.0050.0000.0640.0650.0070.0290.0470.0140.0180.0050.0080.0130.0100.0020.0180.0140.0300.0210.0050.0120.0060.0050.0000.0300.0060.0000.0070.0030.0000.0000.0000.0000.0040.0000.0000.0000.0120.0000.0080.0080.0090.0000.0030.0040.0000.0100.0270.0011.0000.0000.0100.0000.0900.0000.0000.0260.0000.0550.0060.0050.0000.0180.0040.0250.0580.0320.0000.0080.0110.0130.0080.0030.0100.0010.0140.003
state_VT0.0580.0160.0140.0000.0000.0000.0000.0000.0000.0000.0130.0000.0020.0070.0110.0590.0000.0000.0000.0070.0080.0000.0000.0070.0600.0780.0000.0440.0360.0240.0220.0020.0170.0130.0060.0280.0160.0160.0480.0050.0040.0100.0050.0040.0000.0260.0050.0000.0060.0030.0000.0000.0000.0000.0040.0000.0000.0000.0100.0000.0070.0070.0070.0000.0020.0030.0000.0090.0230.0000.0001.0000.0080.0000.0750.0000.0000.0000.0000.0510.0070.0060.0000.0160.0450.0100.0560.0160.0160.0230.0050.0110.0190.0140.0000.0140.0080.022
state_WA0.2040.1710.0650.0980.0000.0000.0000.0020.0300.0120.0210.0100.0110.0230.0130.0070.0060.0170.0190.0080.0140.0030.0120.0400.0020.0660.1280.0650.0170.1510.0990.0720.0000.0330.0130.1020.0700.0680.0820.0470.0250.0560.0310.0250.0000.1350.0310.0050.0340.0210.0020.0110.0130.0000.0240.0120.0000.0000.0540.0030.0380.0390.0410.0000.0200.0240.0000.0480.1220.0140.0100.0081.0000.0060.0560.0000.0030.0130.0000.0510.0180.1610.0050.0630.0160.1420.0240.0910.0170.0100.0240.0040.0580.0130.0030.0130.0210.016
state_WI0.0590.0700.0060.0000.0020.0000.0000.0000.0020.0000.0230.0000.0020.0050.0080.0170.0200.0010.0120.0000.0000.0020.0000.0000.0000.0090.0310.0560.0000.0350.0170.0130.0130.0260.0120.0200.0120.0280.0310.0120.0020.0080.0030.0020.0000.0200.0030.0000.0040.0010.0000.0000.0000.0000.0020.0000.0000.0000.0080.0000.0050.0050.0050.0000.0010.0020.0000.0060.0180.0000.0000.0000.0061.0000.0700.0000.0000.0000.0000.0400.0050.0040.0000.0130.0020.0450.0110.0300.0150.0060.0220.0090.0070.0340.0130.0150.0320.013
status_Active0.0760.2400.3890.0450.0200.0230.0180.0070.0010.1260.0390.1030.0860.3820.1220.0040.0060.0240.0330.0120.0040.0250.0080.0190.0260.0690.0570.0730.0090.1600.0110.1140.0490.0120.0220.0110.1780.0750.1130.0470.1770.0360.0390.0670.0000.0810.0350.0560.0320.0370.0300.0230.1120.0240.0070.0260.0180.0000.0540.0410.0820.0650.0910.0110.0440.0490.0240.0820.0320.0300.0900.0750.0560.0701.0000.0000.0090.0290.0000.5770.0840.0730.0120.1850.0510.0320.0140.0270.0200.0200.0000.0110.0240.0060.0040.0090.0180.008
status_Closed0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
status_Coming Soon0.0000.0000.0110.0000.0040.0000.0000.0000.0000.0000.0000.0000.0050.0030.0000.0000.0000.0000.0000.0000.0010.0000.0010.0000.0000.0150.0010.0030.0130.0080.0050.0050.0050.0000.0000.0020.0020.0000.0050.0000.0000.0220.0180.0010.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0000.0000.0000.0000.0000.0010.0000.0000.0000.0030.0000.0090.0001.0000.0000.0000.0260.0020.0010.0000.0080.0000.0050.0110.0030.0010.0000.0060.0050.0010.0000.0000.0000.0000.000
status_Contingent0.0050.0050.0410.0000.0040.0000.0000.0000.0000.0000.0040.0000.0160.0100.0170.0030.0110.0040.0070.0040.0060.0080.0000.0100.0080.0290.0070.0700.0220.0190.0020.0270.0110.0180.0090.0240.0240.0140.0210.0060.0070.0150.0090.0060.0000.0370.0080.0000.0350.0050.0000.0020.0030.0000.0060.0020.0000.0000.0150.0000.0110.0060.2020.0000.0050.0060.0000.0080.0340.0030.0260.0000.0130.0000.0290.0000.0001.0000.0000.0800.0110.0100.0000.0250.0060.0460.0000.0090.0050.0060.0110.0040.0060.0020.0030.0030.0010.020
status_For Rent0.0000.0000.0000.0130.0010.0000.0000.0000.0000.0000.0010.0000.0050.0000.0000.0000.0000.0010.0010.0030.0020.0030.0030.0000.0000.0000.0010.0000.0000.0000.0000.0020.0000.0030.0020.0000.0020.0030.0140.0060.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0040.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0020.0000.0030.0000.0000.000
status_For Sale0.0490.0860.1550.0310.0210.0140.0300.0190.0190.0740.0580.0600.0430.2370.1100.0130.0000.0000.0190.0270.0120.0200.0410.0460.0110.0370.0220.0250.0420.0430.0960.0670.0070.0140.0760.0330.1510.0730.0820.0810.1450.0590.0120.0230.0010.0790.0800.0360.0580.0610.0200.0220.0680.0160.0100.0330.0110.0010.0130.0240.1260.1070.0550.0100.0670.0730.0140.0130.0310.0450.0550.0510.0510.0400.5770.0000.0260.0800.0041.0000.2300.1990.0340.5050.1400.0780.0700.0610.0070.0170.0000.0100.0100.0230.0190.0210.0100.005
status_Foreclosure0.0610.0100.0210.0050.0370.0000.0000.0000.0100.0100.0080.0080.0590.0260.0300.0060.0350.0300.0360.0120.0230.0060.0060.0440.0000.0190.0110.0240.0290.0080.0340.0000.0220.0010.0160.0070.0110.0290.0590.0500.0210.0720.0010.0280.0000.0000.0050.0040.0490.0000.0000.0290.0040.0000.0600.0090.0000.0000.0160.0020.0040.0220.0080.0000.0020.0150.0000.0170.0790.0090.0060.0070.0180.0050.0840.0000.0020.0110.0000.2301.0000.0290.0040.0740.0200.1250.0540.0240.0000.0050.0170.0020.0000.0070.0090.0110.0010.010
status_Pending0.0470.0810.1390.0000.0060.0000.0060.0000.0090.0070.0060.0060.0420.0270.0420.0000.0070.0070.0000.0120.0070.0090.0230.0330.0000.0390.0090.0360.0100.0740.0200.0390.0220.0140.0050.0130.0600.0430.0570.0320.0900.0200.0150.0170.0000.0160.0180.0090.0230.0060.0080.0070.0100.0060.0130.0080.0000.0000.0360.0000.0280.0240.0620.0150.0150.0170.0000.0280.0580.0100.0050.0060.1610.0040.0730.0000.0010.0100.0000.1990.0291.0000.0030.0640.0170.0350.0000.0130.0160.0070.0130.0030.0070.0100.0020.0000.0050.000
status_Price Change0.0140.0060.0370.0140.0110.0070.0000.0000.0000.0000.0030.0000.0090.0580.0060.0000.0030.0000.0050.0040.0040.0050.0050.0000.0000.0000.0270.0040.0100.0190.0190.0200.0270.0220.0240.0000.0100.0000.0120.0040.0010.0060.0020.0010.0000.0170.0020.0000.0100.0000.0000.0000.0000.0000.0010.0000.0000.0000.0060.0000.0040.0040.0040.0000.0000.0000.0000.0050.0420.0000.0000.0000.0050.0000.0120.0000.0000.0000.0000.0340.0040.0031.0000.0100.0000.0130.0090.0040.0000.0040.0090.0010.0060.0030.0000.0020.0020.000
status_Therest0.1930.1650.2510.0000.0660.0030.0170.0390.0270.0230.0370.0180.0200.0560.0270.0270.0270.0040.0090.0480.0050.0520.0330.0070.0150.0590.0640.0930.0960.1830.1670.0680.0890.0450.0880.0910.0340.1080.1770.0910.0460.0830.0540.0430.0000.2240.0570.0110.0630.0380.0060.0140.0160.0040.0420.0160.0020.0000.0990.0070.0680.0710.0750.0020.0360.0430.0030.0870.1680.0170.0180.0160.0630.0130.1850.0000.0080.0250.0000.5050.0740.0640.0101.0000.0450.0420.1070.0730.0140.0090.0240.0000.0450.0370.0270.0270.0040.005
status_Under Contract0.0350.0210.0550.0030.0320.0000.0040.0000.0030.0060.0130.0040.0080.0210.0310.0000.0040.0030.0000.0120.0040.0110.0230.0000.0010.0050.0670.0210.0060.0910.0210.0540.0780.0560.0050.0570.0430.0300.0330.0080.0920.0290.1180.0130.0000.0700.0110.0010.0180.0100.0000.0050.0060.0000.0120.0060.0000.0060.1540.0000.0200.0190.0210.0000.0100.0120.0000.1160.0580.0060.0040.0450.0160.0020.0510.0000.0000.0060.0000.1400.0200.0170.0000.0451.0000.0370.0160.0110.0160.0100.0140.0010.0090.0100.0090.0040.0170.000
target0.1300.0930.0930.0130.0800.0120.0360.0180.2120.0280.1300.0210.1120.0270.0250.0520.1240.0330.0980.0520.0720.0290.0290.4110.0290.0570.0720.0470.0670.0680.0870.0500.0570.0250.0330.1000.104-0.0360.1840.4380.0670.2340.0890.1210.0000.0920.0130.0360.0370.0630.0260.0340.0210.0180.1930.0560.0050.0000.0550.0110.0560.2060.2190.0220.0690.0310.0110.0480.1510.0290.0250.0100.1420.0450.0320.0110.0050.0460.0160.0780.1250.0350.0130.0420.0371.0000.1000.0500.0220.0120.0430.0270.0190.0270.0320.0310.0190.019
zipcode_00.0890.0040.0200.0040.0080.0030.0000.0000.0240.0170.0310.0160.0270.0490.0240.0390.0400.0000.0310.0190.0100.0090.0080.0260.1370.2980.0690.1510.1570.0680.0900.0150.0150.0020.0290.0130.0310.0570.0360.0420.0060.2010.0300.0270.0100.1780.0270.0390.0230.0710.0070.0830.0710.0080.0660.0850.0010.0060.0370.0060.0100.0990.0390.0240.0270.0500.0320.0290.0860.0350.0580.0560.0240.0110.0140.0020.0110.0000.0000.0700.0540.0000.0090.1070.0160.1001.0000.1080.0200.0130.0480.0290.0020.0070.0010.0080.0000.013
zipcode_10.0710.0040.0100.0110.0420.0060.0060.0000.0180.0190.0390.0200.0340.0360.0010.0250.0330.0040.0200.0170.0000.0040.0000.0120.0650.1080.0020.1050.1210.0170.0460.0170.0080.0120.0410.0430.0310.0480.0360.0230.0010.0630.0270.0300.0050.1420.0040.0000.0210.0560.0000.0180.0100.0130.0240.0110.0140.0020.0200.0070.0260.0520.0410.0040.0240.0510.0020.0010.0340.0290.0320.0160.0910.0300.0270.0000.0030.0090.0000.0610.0240.0130.0040.0730.0110.0500.1081.0000.0270.0130.0110.0310.0140.0160.0120.0010.0000.000
zipcode_100.0200.0050.0140.0060.0270.0050.0000.0000.0110.0040.0040.0050.0130.0360.0100.0030.0050.0040.0000.0080.0010.0060.0030.0220.0040.0160.0420.0230.0090.0000.0480.0180.0180.0180.0130.0140.0100.0160.0570.0180.0290.0240.0340.0080.0000.0380.0040.0080.0140.0130.0100.0140.0050.0020.0170.0090.0080.0000.0250.0140.0350.0240.0400.0080.0470.0200.0000.0270.0000.0000.0000.0160.0170.0150.0200.0000.0010.0050.0000.0070.0000.0160.0000.0140.0160.0220.0200.0271.0000.0060.0390.0200.0030.0170.0000.0000.0260.000
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zipcode_20.0140.0020.0190.0110.0050.0000.0020.0000.0160.0070.0340.0030.0090.0330.0010.0200.0240.0050.0170.0100.0080.0040.0000.0200.0120.0460.0160.1160.0110.0590.0670.0090.0760.0060.0040.0130.0110.0460.0360.0300.0300.0600.0100.0260.0010.0690.0160.0040.0430.0130.0060.0260.0150.0030.0290.0340.0110.0000.0360.0100.0070.0110.0170.0060.0150.0080.0100.0290.0450.0090.0110.0050.0240.0220.0000.0000.0060.0110.0000.0000.0170.0130.0090.0240.0140.0430.0480.0110.0390.0301.0000.0210.0320.0130.0300.0150.0020.002
zipcode_30.0090.0130.0010.0080.0030.0070.0000.0020.0050.0040.0150.0060.0030.0090.0150.0090.0120.0000.0040.0080.0000.0000.0030.0270.0050.0350.0320.0300.0590.0380.0000.0450.0120.0020.0050.0060.0100.0120.0880.0240.0180.0160.0120.0120.0000.0060.0360.0190.0450.0050.0110.0150.0240.0080.0480.0140.0100.0000.0010.0130.0210.0080.0340.0070.0050.0230.0060.0190.0110.0140.0130.0110.0040.0090.0110.0000.0050.0040.0000.0100.0020.0030.0010.0000.0010.0270.0290.0310.0200.0030.0211.0000.0060.0080.0040.0110.0070.000
zipcode_40.0080.0080.0060.0120.0090.0030.0030.0040.0110.0100.0090.0000.0100.0020.0170.0040.0060.0060.0080.0020.0000.0000.0040.0120.0080.0150.0020.0260.0140.0420.0540.0180.0220.0060.0360.0020.0040.0230.0650.0140.0600.0060.0090.0260.0000.0160.0130.0140.0240.0190.0100.0030.0020.0000.0000.0240.0080.0000.0340.0170.0160.0390.0430.0070.0060.0060.0000.0000.0430.0120.0080.0190.0580.0070.0240.0000.0010.0060.0020.0100.0000.0070.0060.0450.0090.0190.0020.0140.0030.0180.0320.0061.0000.0210.0190.0350.0150.051
zipcode_50.0210.0350.0190.0080.0490.0030.0030.0030.0030.0060.0070.0020.0430.0120.0000.0000.0100.0070.0090.0090.0120.0090.0020.0180.0030.0010.0540.0410.0290.0460.0580.0310.0550.0200.0340.0010.0000.0250.0710.0430.0100.0540.0110.0400.0030.0120.0290.0030.0430.0050.0100.0010.0010.0020.0290.0080.0090.0000.0290.0140.0370.0240.0000.0080.0070.0000.0120.0120.0200.0070.0030.0140.0130.0340.0060.0000.0000.0020.0000.0230.0070.0100.0030.0370.0100.0270.0070.0160.0170.0040.0130.0080.0211.0000.0280.0160.0440.002
zipcode_60.0070.0090.0130.0110.0120.0050.0030.0020.0120.0000.0030.0090.0200.0140.0000.0000.0030.0070.0070.0070.0080.0000.0000.0250.0100.0110.0130.0050.0220.0440.0230.0100.0210.0350.0140.0310.0070.0080.0430.0350.0100.0180.0380.0050.0020.0670.0410.0030.0180.0180.0100.0160.0100.0060.0100.0110.0080.0000.0500.0150.0110.0030.0350.0080.0000.0120.0040.0500.0110.0040.0100.0000.0030.0130.0040.0000.0000.0030.0030.0190.0090.0020.0000.0270.0090.0320.0010.0120.0000.0320.0300.0040.0190.0281.0000.0090.0120.000
zipcode_70.0000.0110.0020.0100.0120.0000.0070.0000.0100.0070.0090.0150.0180.0000.0090.0060.0100.0040.0170.0010.0080.0050.0070.0080.0050.0050.0190.0280.0100.0610.0180.0060.0000.0150.0000.0210.0070.0380.0590.0120.0100.0140.0050.0040.0000.0200.0310.0060.0030.0030.0110.0160.0070.0100.0210.0010.0090.0000.0110.0140.0430.0050.0200.0080.0110.0220.0110.0370.0540.0050.0010.0140.0130.0150.0090.0000.0000.0030.0000.0210.0110.0000.0020.0270.0040.0310.0080.0010.0000.0320.0150.0110.0350.0160.0091.0000.0130.000
zipcode_80.0010.0060.0050.0020.0130.0090.0000.0000.0040.0070.0060.0060.0020.0020.0120.0020.0060.0000.0040.0120.0100.0140.0040.0210.0000.0090.0370.0050.0110.0200.0240.0090.0600.0100.0120.0000.0160.0260.0540.0280.0230.0090.0000.0020.0020.0100.0100.0130.0190.0080.0100.0010.0030.0100.0120.0000.0090.0000.0430.0190.0050.0150.0000.0080.0000.0120.0040.0080.0080.0160.0140.0080.0210.0320.0180.0000.0000.0010.0000.0100.0010.0050.0020.0040.0170.0190.0000.0000.0260.0150.0020.0070.0150.0440.0120.0131.0000.021
zipcode_90.0030.0070.0130.0050.0160.0080.0040.0050.0000.0070.0030.0070.0070.0000.0160.0090.0030.0100.0000.0080.0000.0000.0000.0080.0000.0040.0650.0350.0020.0290.0050.0370.0800.0650.0360.0110.0020.0310.0750.0220.0080.0060.0010.0110.0020.0110.0130.0090.0000.0240.0100.0080.0080.0040.0250.0030.0080.0000.0140.0190.0270.0120.0310.0080.0080.0030.0070.0220.0000.0190.0030.0220.0160.0130.0080.0000.0000.0200.0000.0050.0100.0000.0000.0050.0000.0190.0130.0000.0000.0090.0020.0000.0510.0020.0000.0000.0211.000

Missing values

2025-06-03T17:32:18.757749image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-03T17:32:20.515171image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

bathssqfttargetpool_corrHeating_corrCooling_corrParking_corrfireplace_corrschool_rating_meanschool_distance_minstatus_Activestatus_Closedstatus_Coming Soonstatus_Contingentstatus_For Rentstatus_For Salestatus_Foreclosurestatus_Pendingstatus_Price Changestatus_Thereststatus_Under Contractstate_AZstate_CAstate_COstate_DCstate_DEstate_FLstate_GAstate_IAstate_ILstate_INstate_KYstate_MAstate_MDstate_MEstate_MIstate_MOstate_MSstate_MTstate_NCstate_NJstate_NVstate_NYstate_OHstate_OKstate_ORstate_PAstate_SCstate_TNstate_TXstate_UTstate_VAstate_VTstate_WAstate_WITypeofproperty_apartmentTypeofproperty_condoTypeofproperty_historicalTypeofproperty_landTypeofproperty_miscellaneousTypeofproperty_mobile_homeTypeofproperty_modernTypeofproperty_multi_family_homeTypeofproperty_ranchTypeofproperty_single_family_homeTypeofproperty_therestTypeofproperty_townhousecity_0city_1city_2city_3city_4city_5city_6city_7city_8city_9city_10zipcode_0zipcode_1zipcode_2zipcode_3zipcode_4zipcode_5zipcode_6zipcode_7zipcode_8zipcode_9zipcode_10zipcode_11Year built_0Year built_1Year built_2Year built_3Year built_4Year built_5Year built_6Year built_7
03.01947310000FalseFalseFalseFalseFalse4.01.010.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000000100000000000100000001
13.01930311995FalseTrueTrueTrueFalse3.00.600.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000001000000000001000000001
22.01300669000FalseFalseFalseTrueFalse2.80.300.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00000000001100000000001100000010
33.02839525000TrueTrueTrueTrueFalse7.30.920.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000010000000000010000000011
42.01820499900FalseTrueFalseTrueFalse7.31.100.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000010100000000010100000001
53.02454168800FalseTrueTrueTrueFalse5.30.600.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000001000000000011000000100
62.02203335000FalseFalseFalseFalseFalse3.50.501.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00000000011000000000011100000101
74.03080365000FalseTrueTrueTrueTrue7.01.780.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000011100000000100000000110
84.01612626000TrueTrueTrueFalseFalse5.00.190.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00000000011000000000100100000111
92.01731375000FalseTrueTrueTrueFalse4.01.700.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000100000000000101000001000
bathssqfttargetpool_corrHeating_corrCooling_corrParking_corrfireplace_corrschool_rating_meanschool_distance_minstatus_Activestatus_Closedstatus_Coming Soonstatus_Contingentstatus_For Rentstatus_For Salestatus_Foreclosurestatus_Pendingstatus_Price Changestatus_Thereststatus_Under Contractstate_AZstate_CAstate_COstate_DCstate_DEstate_FLstate_GAstate_IAstate_ILstate_INstate_KYstate_MAstate_MDstate_MEstate_MIstate_MOstate_MSstate_MTstate_NCstate_NJstate_NVstate_NYstate_OHstate_OKstate_ORstate_PAstate_SCstate_TNstate_TXstate_UTstate_VAstate_VTstate_WAstate_WITypeofproperty_apartmentTypeofproperty_condoTypeofproperty_historicalTypeofproperty_landTypeofproperty_miscellaneousTypeofproperty_mobile_homeTypeofproperty_modernTypeofproperty_multi_family_homeTypeofproperty_ranchTypeofproperty_single_family_homeTypeofproperty_therestTypeofproperty_townhousecity_0city_1city_2city_3city_4city_5city_6city_7city_8city_9city_10zipcode_0zipcode_1zipcode_2zipcode_3zipcode_4zipcode_5zipcode_6zipcode_7zipcode_8zipcode_9zipcode_10zipcode_11Year built_0Year built_1Year built_2Year built_3Year built_4Year built_5Year built_6Year built_7
2027833.02672499999TrueTrueTrueTrueTrue4.00.800.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000001011010001000011100001101
2027842.01907287999FalseTrueTrueTrueFalse4.00.100.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00001100001000000011010100100101
2027853.02505384900FalseTrueTrueTrueTrue5.71.310.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00010100111000110101011000101000
2027863.01792280000FalseTrueTrueTrueTrue2.70.190.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000001000000000001000111001
2027872.01829171306FalseTrueTrueTrueTrue2.31.100.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000011010000110101001001010010
2027882.01841252990FalseTrueTrueTrueFalse6.00.300.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000000001001110010000100000001
2027893.01417799000FalseTrueTrueTrueFalse3.00.100.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00000110011100110010011101100101
2027903.02000674999FalseTrueFalseFalseFalse4.30.400.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00000010000000001010011000101011
2027913.01152528000FalseTrueFalseTrueFalse4.50.480.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000011100100001111110000101000
2027922.01462204900FalseTrueTrueTrueTrue4.00.300.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00000001111101000001010000000001